LREC 2020 Workshop
Language Resources and Evaluation Conference
11–16 May 2020

7th Workshop on Linked Data in Linguistics(LDL-2020)

PROCEEDINGS

Editors:
Maxim Ionov, John P. McCrae, Christian Chiarcos, Thierry
Declerck, Julia Bosque-Gil, Jorge Gracia

Proceedings of the LREC 2020
7th Workshop on Linked Data in Linguistics(LDL-2020)

Edited by:
Maxim Ionov, John P. McCrae, Christian Chiarcos, Thierry Declerck, Julia Bosque-Gil, and Jorge Gracia

The 7th Workshop on Linked Data in Linguistics was supported by the European Union’s Horizon
2020 research and innovation program under grant agreement no. 825182 through the Prêt-à-LLOD
project, the German Ministry for Education and Research (BMBF) through the project Linked Open
Dictionaries (LiODi, 2015-2020) and the COST Action CA18209, NexusLinguarum: European
network for Web-centred linguistic data science.

ISBN: 979-10-95546-36-8
EAN: 9791095546368

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7th Workshop on Linked Data in Linguistics (LDL-2020). Building tools
and infrastructures
Past years have seen a growing interest in the application of knowledge graphs and Semantic Web
technologies to language resources, and their publication as linked data on the Web. As of today, alarge amount of language resources were either converted or created natively as linked data on the basisof data models specifically designed for the representation of linguistic content. Examples are wordnets,
dictionaries, corpora, culminating in the emergence of a Linguistic Linked Open Data (LLOD) cloud(http://linguistic-lod.org/).
Since its establishment in 2012, the Linked Data in Linguistics (LDL) workshop series has become
the major forum for presenting, discussing and disseminating technologies, vocabularies, resourcesand experiences regarding the application of semantic technologies and the Linked Open Data (LOD)
paradigm to language resources in order to facilitate their visibility, accessibility, interoperability,
reusability, enrichment, combined evaluation and integration. The LDL workshops contribute to thediscussion, dissemination and establishment of community standards that drive this development, mostnotably the OntoLex-lemon model for lexical resources, as well as standards for other types of language
resources still under development.
The workshop series is organized by Open Linguistics, founded 2010 as a Working Group of
the Open Knowledge Foundation1 with close involvement of related communities, such as W3C
Community Groups, and international research projects. It takes a general focus on LOD-based
resources, vocabularies, infrastructures and technologies as means for managing, improving and using
language resources on the Web. As technology and resources increasingly converge towards a LODbased ecosystem, this year we particularly encouraged submissions on Linked-Data Aware Tools andServices and Linked Language Resources Infrastructure, i.e. managing, curating and applying LLOD
technologies and resources in a reliable and reproducible way for the needs of linguistics, NLP anddigital humanities.
After ten years of community work, a critical mass of LLOD resources is already in place, yet, there
is still a need to develop a robust ecosystem of tools that consume linguistic linked data. Recently
started research networks and European projects are working in the direction of building sustainableinfrastructures around LRs, with linked data as one of the core technologies. LDL-2020 is thus supportedby the COST Action "European network for Web-centred linguistic data science" (NexusLinguarum) andtwo Horizon 2020 projects, the European Lexicographic Infrastructure (ELEXIS), and Prêt-à-LLOD,
which focuses on providing an infrastructure for linguistic data to be ready to use by state-of-the-art
technologies.
With a focus on building tools and applications, the 7th Workshop on Linked Data in Linguistics(LDL-2020) was organized in conjunction with the 12th Language Resource and Evaluation Conference(LREC-2020). We received a total of 23 submissions out of which 12 were accepted (acceptance rate52%). Due to Covid-19, LDL-2020 was not taking place as a physical meeting, but as a virtual event2 .
Presentations of the accepted papers were organized in three groups with four presentations each, onmodelling, applications and lexicography, respectively.
12

https://groups.google.com/forum/#!forum/open-linguistics
Details and the program are available at http://ldl2020.linguistic-lod.org/program.html

iii

Modelling
In Towards an ontology based on Hallig-Wartburg’s Begriffssystem for Historical Linguistic Linked Data
Tittel et al. compare two strategies for the LOD modelling of a conceptual system that is used in historicallexicography and lexicology, based on SKOS and OWL, respectively, and with examples from medieval
Gascon and Italian.
In Transforming the Cologne Digital Sanskrit Dictionaries into Ontolex-Lemon, Mondaca and Rau
evaluate two strategies for transforming TEI/XML data into OntoLex-Lemon, the enrichment of TEI
XML with RDFa data, and a native RDF modelling. This evaluation tackles an important issue forapplications in Digital Humanities as the TEI does not provide commonly accepted specifications forinterfacing traditional XML-based workflows and Linked Open Data technologies.
In Representing Temporal Information in Lexical Linked Data Resources, Khan describes recent
developments on his extension of the OntoLex-Lemon vocabulary with diachronic lexical information
with examples from the Oxford English Dictionary and an etymological dictionary.
In From Linguistic Descriptions to Language Profiles, Shafqat Mumtaz Virk et al. introduce the conceptof language profiles as structured representations of various types of knowledge about a natural language,
they describe how to semi-automatically construct such data from descriptive documents and they
develop a language profile of an example language.
Applications and Infrastructures
While overarching linked data-based infrastructures are only emerging, numerous applications of thistechnology are being reported.
With Terme-à-LLOD: Simplifying the Conversion and Hosting of Terminological Resources as Linked
Data, Maria Pia di Buono et al. simplify the transformation and publication of terminology data by
virtualization: A preconfigured virtual image of a server can thus be used to simplify installation oftransformation and hosting services for terminological resources as linked data.
Frank Abromeit et al. introduce Annohub – Annotation Metadata for Linked Data Applications, a dataset
and a portal that provides metadata about annotation and language identification for annotated language
resources available on the web. Annohub builds on metadata repositories to identify language resources,on automated routines for classifying languages and annotation schemes, a broad range of transformers
for various corpus formalisms and human curation for quality assurance.
Salgado et al. address Challenges of Word Sense Alignment for Portuguese Language Resources
and report on a comparative study between the Portuguese Academy of Sciences Dictionary and the
Dicionário Aberto. Word sense alignment involves searching for matching senses within dictionary
entries of different lexical resources and linking them, implemented here by means of Semantic Web
technologies.
In A Lime-Flavored REST API for Alignment Services, Fiorelli and Stellato describe a REST API to
enable the participation of downstream alignment services an orchestration framework for ontologyalignment. Using explicit metadata about the input ontologies, other resources and the task itself, areport is produced that summarizes characteristics and alignment strategies. For the lexical content of
the input ontologies and external language resources, the report uses the Lime module of the OntoLexLemon model.

iv

Lexicography
Abgaz describes on-going work on Using OntoLex-Lemon for Representing and Interlinking
Lexicographic Collections of Bavarian Dialects, comprising two main components, a questionnaire withdetails about questions, collectors, paper slips etc., and a lexical dataset which contains lexical entries(answers) collected in response to the questions. The paper describes how the original TEI/XML format
is transformed into Linguistic Linked Open Data to produce a lexicon for Bavarian Dialects.
With Linguistic Linked (Open) Data and, especially, the OntoLex vocabulary now being widely adapted
throughout lexicography, there is a demand for tools, both for exploiting linked lexical data and for
creating a user-friendly access to it. In Involving Lexicographers in the LLOD Cloud with LexO, an
Easy-to-use Editor of Lemon Lexical Resources, Bellandi and Giovannetti describe LexO, a collaborative
web editor of OntoLex-Lemon resources.
As for tools for lexicography, Gun Woo Lee et al. describe Supervised Hypernymy Detection in Spanish
through Order Embeddings, based on a hypernymy dataset for Spanish built from WordNet and the useof pretrained word vectors as input.
Finally, Nielsen reports on Lexemes in Wikidata, i.e., the way that Wikidata records data about lexemes,
senses and lexical forms and exposes them as Linguistic Linked Open Data and the growth anddevelopment of this data set since its first establishment in 2018.

v

Organizers:
Maxim Ionov, Goethe University Frankfurt (Germany)
John P. McCrae, National University of Ireland, Galway (Ireland)
Christian Chiarcos, Goethe University Frankfurt (Germany)Thierry Declerck, DFKI GmbH (Germany) and ACDH-ÖAW (Austria)
Julia Bosque-Gil, University of Zaragoza (Spain)
Jorge Gracia, University of Zaragoza (Spain)

Program Committee:
Paul Buitelaar, Insight (Ireland)
Steve Cassidy, Macquarie University (Australia)
Philipp Cimiano, University of Bielefeld (Germany)
Gerard de Melo, Rutgers University (USA)Francesca Frontini, Université Paul-Valéry (France)
Jeff Good, University at Buffalo (USA)Dagmar Gromann, Vienna University (Austria)
Yoshihiko Hayashi, Osaka University, Waseda University (Japan)
Fahad Khan, ILC-CNR (Italy)
Bettina Klimek, University of Leipzig (Germany)
Elena Montiel-Ponsoda, Universidad Politécnica de Madrid (Spain)
Steve Moran, Universität Zürich (Switzerland)
Roberto Navigli, “La Sapienza” Università di Roma (Italy)
Sebastian Nordhoff, Language Science Press Berlin (Germany)
Petya Osenova, IICT-BAS (Bulgaria)
Antonio Pareja-Lora, Universidad Complutense Madrid (Spain)
Laurent Romary, INRIA (France)
Felix Sasaki, Cornelsen Verlag GmbH Berlin (Germany)
Andrea Schalley, Karlstad University (Sweden)
Gilles Sérasset, University Grenoble Alpes (France)
Armando Stellato, University of Rome, Tor Vergata (Italy)
Marieke van Erp, KNAW Humanities Cluster (The Netherlands)
Piek Vossen, Vrije Universiteit Amsterdam (The Netherlands)vi

Table of Contents
Towards an Ontology Based on Hallig-Wartburg’s Begriffssystem for Historical
Linguistic Linked Data
Sabine Tittel, Frances Gillis-Webber and Alessandro A. Nannini . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1
Transforming the Cologne Digital Sanskrit Dictionaries into Ontolex-Lemon
Francisco Mondaca and Felix Rau . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11
Representing Temporal Information in Lexical Linked Data Resources
Fahad Khan . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 15
From Linguistic Descriptions to Language Profiles
Shafqat Mumtaz Virk, Harald Hammarström, Lars Borin, Markus Forsberg and
Søren Wichmann . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 23
Terme-à-LLOD: Simplifying the Conversion and Hosting of Terminological Resources
as Linked Data
Maria Pia di Buono, Philipp Cimiano, Mohammad Fazleh Elahi and Frank Grimm . . . . . . . . . . . . 28
Annohub – Annotation Metadata for Linked Data Applications
Frank Abromeit, Christian Fäth and Luis Glaser . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 36
Challenges of Word Sense Alignment: Portuguese Language Resources
Ana Salgado, Sina Ahmadi, Alberto Simões, John Philip McCrae and Rute Costa . . . . . . . . . . . . . 45
A Lime-Flavored REST API for Alignment Services
Manuel Fiorelli and Armando Stellato . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 52
Using OntoLex-Lemon for Representing and Interlinking Lexicographic Collectionsof Bavarian Dialects
Yalemisew Abgaz . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 61
Involving Lexicographers in the LLOD Cloud with LexO, an Easy-to-use Editor of Lemon
Lexical Resources
Andrea Bellandi and Emiliano Giovannetti . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 70
Supervised Hypernymy Detection in Spanish through Order Embeddings
Gun Woo Lee, Mathias Etcheverry, Daniel Fernandez Sanchez and Dina Wonsever . . . . . . . . . . . . 75
Lexemes in Wikidata: 2020 status
Finn Nielsen . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 82

vii

Proceedings of the 7th Workshop on Linked Data in Linguistics (LDL-2020), pages 1–10
Language Resources and Evaluation Conference (LREC 2020), Marseille, 11–16 May 2020c European Language Resources Association (ELRA), licensed under CC-BY-NC

Towards an Ontology Based on Hallig-Wartburg’s Begriffssystem for Historical
Linguistic Linked Data
Sabine Tittel, Frances Gillis-Webber, Alessandro A. Nannini

Heidelberg Academy of Sciences and Humanities, University of Cape Town, University of Vienna
Heidelberg, Germany, Cape Town, South Africa, Vienna, Austria
sabine.tittel@urz.uni-heidelberg.de, fran@fynbosch.com, alessandro.alfredo.nannini@univie.ac.at
Abstract
To empower end users in searching for historical linguistic content with a performance that far exceeds the research functions offered by
websites of, e.g., historical dictionaries, is undoubtedly a major advantage of (Linguistic) Linked Open Data ([L]LOD). An important
aim of lexicography is to enable a language-independent, onomasiological approach, and the modelling of linguistic resources following
the LOD paradigm facilitates the semantic mapping to ontologies making this approach possible. Hallig-Wartburg’s Begriffssystem(HW) is a well-known extra-linguistic conceptual system used as an onomasiological framework by many historical lexicographicaland lexicological works. Published in 1952, HW has meanwhile been digitised. With proprietary XML data as the starting point, ourgoal is the transformation of HW into Linked Open Data in order to facilitate its use by linguistic resources modelled as LOD. In thispaper, we describe the particularities of the HW conceptual model and the method of converting HW: We discuss two approaches, (i) the
representation of HW in RDF using SKOS, the SKOS thesaurus extension, and XKOS, and (ii) the creation of a lightweight ontology
expressed in OWL, based on the RDF/SKOS model. The outcome is illustrated with use cases of medieval Gascon, and Italian.
Keywords: Historical Linguistics, Linked Open Data, Ontology Authoring

1.

Introduction

ticularly for historical language data with non-standardised
word spelling. To facilitate access independent from thewords and their formal representations, the data modellingmust, hence, also be enriched by semantic mapping (of entries, senses, concepts) to appropriate ontologies that depict the ‘real world’ (DBpedia3 , AGROVOC4 , AAT5 , etc.).
The use of an external extra-linguistic ontology as a crossmapping hub for linguistic resources, especially for historical resources, is able to overcome the typical, word-form
driven research scenario. This is facilitated by OntoLexlemon and its “principle of semantics by reference in thesense that the semantics of a lexical entry is expressed byreference to an individual, class or property defined in an
ontology” (Cimiano et al., 2016, 2.1). One such ontology—
in the philosophical meaning of the term—is the so-called
Hallig-Wartburg (HW), first published in 1952 (2 1963): Begriffssystem als Grundlage für die Lexikographie (Hallig
and von Wartburg, 1963). In this paper, we focus on the useof HW by linguistic resources and on its transition from aprinted book to an LOD resource in order to facilitate its
use by linguistic resources on the Semantic Web.
The remainder of the paper is structured as follows: In
section 2., we describe the role of HW for linguistic resources of historical language stages that have been or intend to be modelled as LOD. In section 3., we discuss anattempt to convert HW from the original book, via an XML
digitisation, into an LOD resource that can be used for semantic mapping. In light of the requirements of the LOD
paradigm, we first evaluate a thesaurus-like RDF/SKOS
model in section 3.1.; in section 3.2., we discuss its further
conversion to an ontological model, and we show its practical application with the use case of data from two historical

As the most solid grounding of the Semantic Web, theLinked Data (LD) paradigm is used to represent and interlink structured data on the web. The standard proposed by
the W3C for representing LD (LOD respectively, with ‘O’symbolising open access) is the graph data model Resource
Description Framework (RDF) that represents data in theform of triples with subject, predicate, and object, eachidentified through URIs that are accessible via HTTP (Cyganiak et al., 2014). There are many advantages to representing linguistic resources in RDF, and applying LD principles to them, such as structural and conceptual interoperability, uniform access through standard Web protocols,and resource integration and federation (Chiarcos et al.,2013). Representing dictionary data as Linguistic Linked
Open Data (LLOD) is a very promising approach, especially as it allows for interoperability among different lexicographic resources through the use of common vocabularies that have emerged for the modelling of linguistic data.
The OntoLex-lemon vocabulary (Cimiano et al., 2016) has
been established as the de facto standard RDF data modelfor LLOD; it provides the framework for the representationof language data such as lexical entries, their written representations, and their meanings. The data modelled with
OntoLex-lemon can easily be integrated by linking to external resources, such as ontologies for linguistic annotations(e.g., LexInfo1 ), and extra-linguistic information, such asplace names (e.g., TGN2 ). We point out that the typicalscenario of (historical) linguistic research is characterisedby poor data accessibility through searching for words andtheir formal representations across resources of different
languages and language stages. This scenario hampers semantic driven research of the meanings of the words, par-3

https://wiki.dbpedia.org/ [12-02-2020].
http://agrovoc.uniroma2.it/agrovoc/
agrovoc/en/ [13-02-2020].
5
https://www.getty.edu/research/tools/
vocabularies/aat/ [12-02-2020].
41

https://lexinfo.net/ [12-02-2020].
https://www.getty.edu/research/tools/
vocabularies/tgn/index.html [12-02-2020].
2

1

dictionaries, DAG and LEI, in section 4. Our approach reveals difficulties and shortcomings both with respect to are-engineering of the ontological model and to the conceptual scheme of HW itself, which we discuss in section 5.

2.

It is clear that a concept must be communicated by a sign,and, indeed, the HW concepts are denoted by words of theFrench language. However, these words are only vehicles
and, thus, arbitrary: HW makes it explicit that the words,
e.g., ‘La mer’, are mere symbols of the concepts and not
to be misunderstood as lexemes of the French lexicon (ib.
16; 72). This can be illustrated by, e.g., périodique (periodical) and quotidien (daily) that are both sub-concepts ofthe concept of fois (time [occasion]), not of ‘period’ and‘day’, respectively (ib. 17). As a consequence, concepts
may occur several times (with cross-references), e.g., ‘fishing’ both as an occupation and a sport (ib. 73). The authorsof HW were aware of possible misunderstandings and pointout that a particular identification of the emblematic character of the French words, e.g., through square brackets,
would have been useful but that they refrained from this forthe sake of readability (ib.).
The concepts of the upper six levels of the hierarchy
are denoted by French non-lexicalised categories, e.g.,‘L’univers’, ‘Le ciel et l’atmosphère’, and ‘Le ciel et les
corps célestes’, and, additionally, the concepts are identified by a system of capital letters between A and C, followed by Roman numerals, Arabic lower case letters, etc.:‘A’, ‘A I’, ‘B II h’, etc. This six-level hierarchy forms
the ‘Plan’ with 524 concepts, the outline with the logical abstraction of concepts representing broader, conceptual fields, cf. HW 101–112 (Fig. 1).

Onomasiological Lexicography and theuse of Hallig-Wartburg’s Begriffssystem

Traditional lexicography either follows a semasiologicalapproach in presenting dictionary data, i.e., the data is ordered by the words, or an onomasiological approach, i.e.,the data is ordered by the meaning of the words. For an
onomasiological approach, a thesaurus-like categorisationof the world is needed as a structuring means. Resources referred to as thesauri include the Historical Thesaurus of the
Oxford English Dictionary (HTOED) (Kay, 2009), Roget’s
Thesaurus of English words and phrases (first edition London 1852, Davidson (2002)), and Dornseiff’s Der deutsche
Wortschatz nach Sachgruppen (Dornseiff, 1934). Possibly
the best-known example of a thesaurus-like categorisationof the world used within Romance philology and the reference work of the discipline is Hallig-Wartburg.

2.1.

Structure of Hallig-Wartburg

Hallig’s and Wartburg’s Begriffssystem—German for ‘system of concepts’—is a conceptual scheme in that it is acontrolled vocabulary with a hierarchically structured setof concepts. At first glance, it seems to be a thesauruslike resource. However, ISO 25964 defines a thesaurus asa “controlled and structured vocabulary in which concepts
are represented by terms, organized so that relationships
between concepts are made explicit, and preferred terms
are accompanied by lead-in entries for synonyms or quasisynonyms”, a term being a “word or phrase used to labela concept” and a concept being a “unit of thought” (International Organization for Standardization, 2011). The
terms come from the vocabulary of one or several natural language(s) meaning that they are lexicalised in that
language and typically expressed with equivalence relationships (synonyms, quasi-synonyms or antonyms) in thethesaurus (Kless et al., 2012a; Kless et al., 2012b); cp.
also Helou et al. (2014) on ontology entities expressedin natural language by associating them with terms. The
lexicalisation of the labelling terms is the decisive factor
for the classification of HW as not compliant with ISO
25964. HW does not provide lexicalised terms in a natural language. HW, unlike thesauri such as HTOED, Roget, and also the thesaurus-like, lexical database WordNet(Fellbaum, 1998), does not spring from a list of words ofa natural language (the ‘terms’), e.g., of a (semasiologically structured) dictionary, word list or similar source.
Instead, it is meant to be a resource for the use of, e.g.,
onomasiologically structured dictionaries: It is an extralinguistic reference system of the real world reflecting themodel of thought of a ‘talented average person’ (HW 12),
independent from language and with an a priori character (“ein empirisches, aus sprachlichen Allgemeinbegriffen bestehendes, [. . . ] auf phänomenologischer Grundlage
beruhenden Gliederungsprinzipien gestaltetes außersprachliches Bezugssystem”, ib. 21). HW contains approx. 1675
non-lexicalised concepts ordered in a nine-level hierarchy.

Figure 1: The ‘Plan’ (extract), HW 103.
In HW 113–229, the six conceptual levels of the ‘Plan’ arethen further extended by another, up to three-level hierarchy
of approx. 1,150 finer-grained concepts for “lexicography
proper as represented by the ‘words’ classified in its application” (Orr, cited by HW 20, footnote 4), which we willrefer to as ‘Application’ in the following (Fig. 2). These
concepts are not consecutively numbered.

Figure 2: Finer-grained ‘Application’ (extract), HW 141.
Thesauri (and this applies to a conceptual scheme such as2

HW as well) establish hierarchical relationships and associative relationships between concepts. The hierarchical relationships can be generic, a whole-part relation, anda concept-instance relation; the associative relationships
exist between hierarchically unrelated but semantically orconceptually related concepts (Kless et al., 2012a, 135f.).
HW contains hierarchical (both generic and whole-part relations) and also associative relationships between the concepts (HW 18); neither cyclic hierarchical relationships nororphans. HW prioritises the hierarchical over the associative classification but deliberately prefers the latter in cases
where an association seems more ‘natural’ (ib.), particularly in fields where the concepts are closely connected to
specialised domains, such as house building and hunting.
With this approach to classification, HW wants to take account of the fact that every language has its own peculiarinterpenetration of systematics and non-systematics, whichis reflected in the linguistic interpretation of the world (ib.)
E.g., the concept ‘construire’ (to construct) is neither hierarchically allocated to ‘L’action’ (B II h 3) [together with‘faire’ (to make) and ‘créer’ (to create)], nor to ‘L’espace’(space, C I e) [together with ‘assembler’ (to assemble)].
Instead, it is associated to the concept of house building,i.e., ‘La construction’ (B III b 7 bb, sub ‘L’habitation, lamaison’). The concept ‘miette’ (crumb) is logically a subconcept of ‘morceau’ (part, sub-concept of C I d ‘Le nombre et la quantité’) but associated to the concept ‘Le pain, lapâtisserie’ (bread, patisserie, B I k 1 cc 2 ), and ‘saumure’(brine) is a concept associated to ‘La viande’ (meat, B I k
1 cc 1). An example for a hierarchical, whole-part relationis the relation of the concept ‘les narines’ (nostrils) to its
superordinate concept ‘Le corps et les membres’ (the bodyand its parts).
The concepts and their classification reveal problematiccongruencies, wrong hierarchisation, and inconsistencies6 :1. On levels 1-6, we find the identical concept‘Généralités’ 27 times, semantically disambiguatedthrough its place in the hierarchy, e.g., as a subconcept of ‘Les arbres’; these concepts can be suppressed since one could simply refer to the respective
superordinate concept. On levels 7-9, ‘esp.’ (abbreviating espèces, sub-species, e.g., of the apple) occurs.
2. On levels 8 and 9, we find the string ‘etc.’ as a concept
denomination.
3. On levels 7-9, some concepts are followed by references to homonymic concept denominations (printed
in italics, separated by a comma), e.g., ‘port, v. aussi
p. 197a’.
4. On levels 7-9, some concept denominations are specified through German definitions. In some cases, thisaims at the semantic disambiguation of homonymic
concept denominations within the same superordinate
concept, e.g., ‘beau-père “Schwiegervater” ’ (fatherin-law) / ‘beau-père “Stiefvater” ’ (stepfather).
5. C II a 17 ‘La phonétique’ is on the same hierarchy
level as C II a 18 ‘La linguistique’ but should be a
sub-concept of the latter.

6. We find ‘alchimie’ falsely classified under A II e ‘Lesmétaux’ which is a sub-concept of the top concept A‘L’Univers’. However, this top concept should contain
only sub-concepts related to organic and inorganic nature, and not to human activities (HW 89).
7. Similarly, under A IV ‘Les animaux’ we find ‘Les
animaux fabuleux’ (fabulous beasts) and its subconcepts ‘phénix’ (phoenix) and ‘dragon’ (dragon),concepts that cannot be separated from human conception and should, thus, rather be associated to B II e
‘L’imagination’.
8. A classification inconsistency is the presence of the
sub-concept ‘Le tabac’ (tobacco, B I k 1 dd) under‘Les aliments’ (food, B I k 1), as if tobacco were food.

2.2.

Lexicographical and Lexicological
Resources using Hallig-Wartburg

HW has been chosen by numerous lexicographical and lexicological works as a means of semantic structure. The mostcomprehensive Französisches Etymologisches Wörterbuch(FEW) (von Wartburg, since 1922) is a dictionary of the
Galloromance languages and dialects covering the period
from the middle ages until today, structured by the alphabetical order of the etyma of the treated word families. The words of unknown or uncertain origin are treatedin vol. 21–23 where they are grouped onomasiologically,ordered by the HW concepts. The HW concepts form
the structural backbone of the dictionaries Dictionnaire
onomasiologique de l’ancien occitan (DAO) (Baldinger,
1975 to 2005) and the Dictionnaire onomasiologique del’ancien gascon (DAG) (Baldinger, since 1975): both follow HW to structure the editing and publishing of the dictionary entries (Glessgen and Tittel, 2018, 805). Semantic criteria are used in the Lessico Etimologico Italiano(LEI) (Pfister, since 1979) to build the structure of very
complex articles, as in the FEW 21–23 (Tancke, 1997,466); in these cases, the lexicographical sections are ordered by semantic categories (in Italian language) thatclosely recall those of HW. Recently, the online editionof the Dictionnaire de l’occitan médiéval (DOM) (Stempel, 1996 to 2013) started evaluating the introduction of
HW concepts to align the entries to those of DAGél.7 The
Dictionnaire étymologique de l’ancien français (DEAF)(Baldinger, since 1971) follows a semasiological approach
but inherits HW categories when it refers to entries of
FEW 21–23. The Mittelhochdeutsche Begriffsdatenbank(MHDBDB8 ) creates an onomasiological database for Middle High German, building on HW (Hinkelmanns, 2019):
the HW categorisation has been further developed with theapplication on the lexis of Middle High German Frauendienst by Ulrich von Lichtenstein (1255) and of Lanzelet
by Ulrich von Zatzikhoven (after 1193) (Schmidt, 1980;
Schmidt, 1988; Schmidt, 1993). Also, many onomasiologically structured lexicological studies on medieval until 16th
century French, Italian, Spanish, Gascon and Occitan resources (literary texts, architecture, Bible, etc.), use HWconcepts, e.g., Bevans (1941) on the Old French vocabu-

6

Naturally, concepts that reflect the zeitgeist of the time of
HW’s creation, e.g. ‘Les costumes nationaux et pittoresques’, areto be found as well.

7
8

3

Personal communication by Maria Selig, DOM.
http://mhdbdb.sbg.ac.at/ [06-02-2020].

lary of Champagne9 , Keller (1953) on the vocabulary usedby Wace (* approx. 1110 – † after 1174), de Man (1956) on
the Brabant language in archival sources 1300-1550, etc.
(Baldinger, 1959, 1091f.).

2.3.

Hallig-Wartburg in Linked Open Data
resources

As a contribution to the emerging linguistic LOD cloud andto expand the inadequately represented historical linguistic
resources, efforts to model these lexicographic resources
as Linked Data have been initiated: The FEW is currently
digitally available as bitmap images10 but a digitisation bymeans of XML is underway (Renders, 2015), and Renders(2019) announces a study on how to model etymological
data of the FEW as LOD. For the electronic version of the
LEI, LEI-Digitale (Prifti, 2019), the LEI editors carry out
feasibility studies on LOD modelling and semantic mapping to HW or to a taxonomy based on HW (Nannini, in
progress). Tittel and Chiarcos (2018) created a RDF data
model for the electronic version of the DEAF (DEAFél)and Tittel (in progress) for DAGél, the electronic complement to the DAG (Glessgen, since 2014). The relaunch of
the MHDBDB (planned for 2020) will include an RDF version of the data (Hinkelmanns, 2019).

3.

ble contents, as shown in List. 1. Alas, it does not contain
information that can easily be exploited for a future hierarchical representation of the category levels, as visualised inFig. 3.
1
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<?xml version="1.0"?><resultset
xmlns:xsi="http://www.w3.org/2001/
XMLSchema-instance"><row><field name="identifier">B I k 1 cc 1</field><field name="concept">La viande</field></row><row><field name="identifier">B I k 1 cc 2</field><field name="concept">Le pain, la pâtisserie</field></row></resultset>

Listing 1: Extract of XML data.

From the Begriffssystem to an Ontology

The representation of HW in RDF, and SKOS or as an ontology, achieves compatibility with other Semantic Web
technologies and is thought to facilitate interoperability
across linguistic resources applying HW as their onomasiological framework. This helps to establish the word-formand language-independent access to these resources: a pivotal motivation to model them as LOD and to include references to the HW concepts. A potential reuse both of HW
and of the linguistic resources using HW is also thought tobe promoted by the fact that the HW RDF graph is easy
to be referenced by other bigger, more comprehensive andmore detailed LOD resources, independent from a natural
language. Also, recall one of the main principles of the LD
paradigm: to provide useful information (in RDF) that isreturned when navigating to a URI, i.e., provide dereferenceable URIs.
However, the native format of the HW is a book publication which, thus, needs to be converted into a format compliant with the LOD paradigm. For the digital editing of
the DAGél, the 524 numbered concepts (the ‘Plan’, Fig. 1)of HW (second edition 1963) have been digitised in 2014
using DAG’s dictionary writing system (Glessgen and Tittel, 2018). The finer-grained approx. 1,150 concepts of the‘Application’ (Fig. 2) were excluded from the digitisation
because the DAGél uses only the concepts of the ‘Plan’ as
its framework. As a first step towards an RDF graph basedon HW, we exported the data as XML from the DAGél’s
database. The XML structure is based on rows with a single XML element field and one attribute with two possi-

Figure 3: Hallig-Wartburg concept hierarchy.

3.1.

Hallig-Wartburg in RDF and SKOS

HW is represented in a standard format of a Knowledge Organisation System (KOS), a system to represent classification schemes, thesauri, taxonomies and similar structures.
The W3C has defined the Simple Knowledge Organization
System (SKOS) which provides a data model and vocabulary for expressing KOSs in RDF (Miles and Bechhofer,2009). Two types of semantic relations are distinguishedby SKOS: hierarchical and associative. The hierarchicalrelation is typically represented by the ‘narrower’ and the‘broader’ property, an associative relation is indicated by
the use of ‘related’. However, the specific nature of concept relations cannot be expressed. The ISO 25964 SKOS
extension (Miles and Brickley, 2004) distinguishes finergrained semantic relations between the concepts and aims
at providing better interoperability between SKOS and thethesaurus standard. It is ideal to explicitly express hierarchical generic and whole-part relationships through the
SKOS-Thes properties ‘broaderGeneric’ and ‘broaderPartitive’ respectively. The associative relation expressing apartitive relationship between concepts can be expressed
through the more specific property ‘relatedPartOf’. Extended Knowledge Organization System (XKOS) was developed to extend SKOS for statistical classification, andone of its features, comparable to the SKOS-thesaurus extension, is to refine SKOS’ semantic properties (Cyganiak

9

Draws on the Questionnaire, the dialectal recordings made by
Rudolf Hallig as a preparation for HW (Christmann and Böckle,
1983, 398).
10
https://apps.atilf.fr/lecteurFEW/ [accessed
05-02-2020].

4

et al., 2017). XKOS is a public working draft of a potential specification and therefore we chose to use the SKOSthesaurus extension to express the semantic relations, although the properties of the latter are still classified as ‘unstable’. Nevertheless, XKOS offers possibilities to define
the classification levels of a KOS which we deem valuablefor our approach. The representation of HW’s hierarchicaland associative relationships is thus straightforward. However, the respective relations are not explicitly expressedin the original source, and a representation in SKOS must
comprise a manual assessment of the relations.
We converted the XML data into RDF and SKOS (including extensions), applying the following rules:
1. Since the HW is concept-based according to ISO
25964, all HW concepts can be represented as SKOS
concepts.
2. To define the hierarchy levels and their respective
members, we include XKOS ‘ClassificationLevel’.
3. We define the three concepts of the top level,
A ‘L’univers’, B ‘L’homme’, and C ‘L’homme etl’univers’, as top concepts of the concept scheme(List. 2, l. 7).
4. We
utilizethecontentofXML<field name="concept"> as the concept
denomination: to emphasize the symbolic characterof the denomination by capitalising all characters,
eliminating French accents and replacing spaces,
punctuation marks, and apostrophes with an underscore, e.g., L_HOMME_ET_L_UNIVERS.
5. We also utilize said content to add a SKOS
‘scopeNote’ providing information about the scope ofthe concept. Aiming at removing possible ambiguity
or misunderstanding of the non-lexicalised information (erroneously as ‘terms’) we deem a scope notethe accurate ‘translation’ of the information given in
HW.
6. In SKOS, preferred and alternative lexical labels canbe used for “generating or creating human-readable
representations of a knowledge organization system”(Miles and Bechhofer, 2009); it is consistent withSKOS to assign (multiple) alternative lexical label(s)but no preferred lexical label to a resource. SKOS
does not specify whether a resource with none of thetwo lexical labels is consistent with the SKOS data
model, however, it is said to be advised to include alexical label “in order to generate an optimum humanreadable display” (ib.). Considering this advice andthe de facto missing terms in HW that could naturally
become lexical labels, we propose to misuse the concept denominations: We allocate an additional function to the French words used as arbitrary symbols
by Hallig and Wartburg interpreting them as ‘terms’
expressed through skos:altLabel, e.g., “Les besoins de l’être humain”. This design decision aims
to compensate for the missing terms but refrains fromdeclaring preferred labels.
7. For backwards compatibility, we preserve the consecutive numbers of the upper six levels as contained in
XML <field name="identifier">, using the
SKOS ‘notation’ property; we define the string lit-

eral by a particular HW specific identification scheme<hwIdentificationScheme>.
8. We eliminate concepts denominated by ‘etc.’, assuming that the linguistic resources using HW as a reference do not classify lexemes under a concept ‘etc.’(approved by the editorial team of the DAGél).
9. Hierarchical generic relations are expressed throughskos-thes:broaderGeneric, e.g., the relation
between ‘La viande’ and ‘Les aliments’ (List. 2,
l. 40), hierarchical whole-part relations throughskos-thes:broaderPartitive, e.g., the relation between ‘les narines’ and ‘Le corps et les membres’ (List. 2, l. 51), and associative relations throughskos-thes:relatedPartOf, e.g., the relation
between ‘miette’ and ‘Le pain, la pâtisserie’ (List. 2,
l. 46). To enable navigation from the top concept level
down into the hierarchy, we include the SKOS ‘narrower’ property (l. 27; 41).
10. We distinguish homonymic concepts within thesame superordinate concept, that are, thus, notdisambiguated by their respective, different superordinate concepts, as follows: We add a number
to the concept denomination and preserve theGerman definitions that are used for the semantic disambiguation as a SKOS ‘editorialNote’,e.g., sub B III a 1 aa 3 (‘La parenté’), ‘beaupère’::BEAU_PERE_1 skos:scopeNote"beau-père"@frskos:editorialNote"Schwiegervater"@de and :BEAU_PERE_2
skos:scopeNote"beau-père"@frskos:editorialNote"Stiefvater"@de.
We chose editorialNote over the ostensibly
obvious SKOS property definition to be able touse the latter for a further knowledge enrichment withaccurate genus-differentiae sense definitions.
11. We eliminate references to pages with homonymic
concepts assuming that this information won’t be ofvalue for semantic integration.
The result is shown in List. 2, the data is provided in Turtle
syntax (Prud’hommeaux and Carothers, 2014).11
1@prefix: <http://example.org/hallig-wartburg#> .

2
3
4
5
6
7

:HW a skos:ConceptScheme ;skos:prefLabel "HW classification scheme"@en ;xkos:numberOfLevels 9 ;xkos:levels ( :HW_Level1 ... :HW_Level9 ) ;skos:hasTopConcept :L_HOMME , :L_UNIVERS , ... .

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:hwIdentificationScheme a rdfs:Datatype ;rdfs:comment "HW concept identification scheme" ;owl:oneOf ("B"ˆˆxsd:string"B I k 1 cc 1"ˆˆxsd:string"B I k 1 cc 2"ˆˆxsd:string ... ) .
:HW_Level1 a xkos:ClassificationLevel ;xkos:depth 1 ;skos:member :L_UNIVERS , :L_HOMME ,:L_HOMME_ET_L_UNIVERS .
:L_HOMME a skos:Concept ;skos:altLabel "L’homme"@fr ;skos:scopeNote "L’homme"@fr ;skos:notation "B"ˆˆ:hwIdentificationScheme;
skos:inScheme :HW ;skos:topConceptOf :HW ;27

11

For the sake of brevity, we suppress (lines of) code that do
not add substantial value, and standard namespaces are assumed
defined the usual way, also in List. 3, 5 and 6.

5

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pressivity and descriptiveness than offered by SKOS relations, also preparing for future extension. The result will
be a lightweight ontology, i.e., an RDF document serialised
in OWL, its benefit over the RDF/SKOS model being better interoperability and the potential for a extra-linguistic
cross-mapping hub for the (historical) linguistic resources
using HW concepts as their onomasiological architecture:
A lightweight ontology based on HW provides a possibility
for resources such as DAGél, LEI, DEAF, and MHDBDB
to create instances of the HW classes.
The HW concepts meet the requirement of reflecting universal categories and the SKOS concepts (instances inSKOS) can thus be represented as classes in OWL (cp.
Baker et al. (2013, 38); Kless et al. (2012b, 406-409)). Thisis a viable approach for creating an ontology in OWL Fullbut its result of course does not have inferencing qualities.
Adding the expressive capabilities to allow for reasoning
over the ontological model requires a re-engineering of the
SKOS model into a formal ontology expressed with OWL
DL, which we will discuss shortly in section 5.
The syntactic conversion from the SKOS model into OWL
Full is not straightforward. The fact that thesauri-like
KOSs express concept relations through basically two kindsof relationships only (hierarchical and associative) makes
them underspecified from the perspective of an ontological model (Kless et al., 2012b). At the same time, thealigning of specific relationships in a thesaurus to relationships in an ontological model is not obvious and lacksof corresponding relata, in particular, associative relationships rarely find their matches (ib. 412). In this paper, wedemonstrate the approach of adopting the relationships expressed by SKOS and its thesaurus extension (ib. 422): Theconversion of the concepts ordered hierarchically by thegeneric relation into class/sub-class relations (expressed bymeans of RDFS ‘subClassOf’) (Brickley and Guha, 2014)
is obvious; skos-thes:broaderPartitive will bepreserved for the hierarchical whole-part relationship, andskos-thes:relatedPartOf for the associative relationship. The lexical label can be expressed through RDFS
‘label’, the SKOS properties ‘scopeNote’ and ‘notation’will be preserved. We conducted a small study representing
sample data of HW as an ontological model, see List. 3.

skos:narrower :L_HOMME_ETRE_PHYSIQUE .
:L_HOMME_ETRE_PHYSIQUE a skos:Concept ;skos:altLabel "L’homme, être physique"@fr ;skos:scopeNote "L’homme, être physique"@fr ;skos:notation "B I"ˆˆ:hwIdentificationScheme ;skos:inScheme :HW ;skos-thes:broaderGeneric :L_HOMME ;skos:narrower :LE_SEXE , :LA_RACE , ... .
:LA_VIANDE a skos:Concept ;skos:altLabel "La viande"@fr ;skos:scopeNote "La viande"@fr ;skos:notation "B I k 1 cc 1"ˆˆ:hwIdentificationScheme ;skos:inScheme :HW ;skos-thes:broaderGeneric :LES_ALIMENTS ;skos:narrower :VIANDE , :JAMBON , :LARD ... .
:MIETTE a skos:Concept ;skos:altLabel "miette"@fr ;skos:scopeNote "miette"@fr ;skos:inScheme :HW ;skos-thes:relatedPartOf :LE_PAIN_LA_PATISSERIE .
:LES_NARINES a skos:Concept ;skos:altLabel "les narines"@fr ;skos:scopeNote "les narines"@fr ;skos:inScheme :HW ;skos-thes:broaderPartitive :LE_CORPS_ET_LES_MEMBRES .

Listing 2: Extract of RDF data.
We have considered including the Lemon-tree vocabulary
into the modelling. Lemon-tree has specifically been designed to model lexicographical thesaurus-like resources
as LD, bridging SKOS and the OntoLex-lemon vocabulary (Stolk, 2019). Yet, for the modelling of HW, following the examples given by Lemon-tree, only SKOS andXKOS would be used, hence the advantage would not be
obvious.12 The MHDBDB has created a SKOS model ofthe onomasiological framework (extending HW) that structures the data.13 However, its design differs significantly
from the result of our attempt: The model excludes boththe original HW identifiers and the French concept denominations. Instead, concept denominations have been translated to German and English, and they are treated as lexical
terms, expressed through the SKOS property ‘prefLabel’.
The model expresses the relationships solely as hierarchical generic through SKOS ‘broader’ (not using the inverse
relation ‘narrower’, resulting in the fact that a navigationfrom a top level down is not possible). In any case, it has
become clear that an LOD compliant model of HW presents
a desideratum in the discipline of historical linguistic data.

3.2.

Towards an Ontological Model

The HW RDF/SKOS model is compliant with the LOD
paradigm but it is a representation close to the book published in 1953. With the means of a KOS, it lacks of conceptual abstraction, nuanced semantic relations, and information integration for interoperability (cp. Soergel et al.
(2006)). The Web Ontology Language (OWL) (Bechhofer
et al., 2004) is a popular W3C recommended format to express ontologies, offering an alternative means for porting
KOSs to the Semantic Web. The next step is, thus, to construct an ontological model of the HW in OWL on the basis of the RDF/SKOS model. This will allow for more ex-

1
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15

A linguistic resource could, however, use Lemon-trees’s object property isSenseInConcept to relate a “lexical sense to aconcept that captures its meaning to some extent (that is, partiallyor even fully)” (Stolk, 2019).
13
We thank Peter Hinkelmanns, MHDBDB, for making themodel available to us and for sharing thoughts on how to model
HW in SKOS.

<owl:Ontology rdf:about="https://example.org/halligwartburg-ontology#"><dct:title xml:lang="en">Hallig-Wartburg Ontology</dct:title><vann:preferredNamespacePrefix>hw</vann:preferredNamespacePrefix><dct:description xml:lang="en">Ontology based on ...
</dct:description><owl:versionInfo rdf:datatype="http://www.w3.org/
2001/XMLSchema#string">1.0.0</owl:versionInfo></owl:Ontology>

16
17

12

<rdf:RDF xmlns="https://example.org/hallig-wartburgontology#">

3

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25
26
27

6

<!-- datatype properties --><owl:DatatypeProperty rdf:about="https://lod.academy/
hw-onto/ns/hw#hwIdentificationScheme"><rdfs:label xml:lang="en">HW Identification Scheme</rdfs:label><rdfs:range><rdfs:Datatype><owl:oneOf>...</owl:oneOf></rdfs:Datatype></rdfs:range></owl:DatatypeProperty>

28
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<!-- classes -->We point out that a finer-grained concept for the Old<owl:Class rdf:about="https://example.org/halligGascon lexeme bacon is available, i.e., JAMBON (ham).
wartburg-ontology#LA_VIANDE"><skos:scopeNote xml:lang="fr">La viande</skos:scopeNote>
However, DAGél only uses the numbered concepts of<skos:notation rdf:datatype="https://lod.academy/
HW’s‘Plan’ (Fig. 1) and thus refers to the super-concept
hw-onto/ns/hw#hwIdentificationScheme">
B I k 1 cc 1</skos:notation>
LA_VIANDE. As a consequence, a manual post-processing<rdfs:label xml:lang="fr">La viande</rdfs:label>should include replacing LA_VIANDE by JAMBON. Please<rdfs:subClassOf rdf:resource="https://example.org/
hallig-wartburg-ontology#LES_ALIMENTS"/>note that, in List. 5, l. 11, we use the language tag for</owl:Class>
Old Gascon oc-x-40000006, a shortened form that ex<owl:Class rdf:about="https://example.org/halligwartburg-ontology#MIETTE">
pands to oc-x-02q35735-241050--1500 using the<skos:scopeNote xml:lang="fr">miette</skos:scopeNote>
Web application for generating and decoding language tags
<rdfs:label xml:lang="fr">miette</rdfs:label><rdfs:subClassOf rdf:resource="https://example.org/
at https://londisizwe.org/language-tags/
hallig-wartburg-ontology#HWCat"/>[07-02-2020].14<skos-thes:relatedPartOf rdf:resource="https://example.
org/hallig-wartburg-ontology#LE_PAIN_LA_PATISSERIE"/>
Italian cantuccino. The digitisation of the LEI and its</owl:Class><owl:Class rdf:about="https://example.org/hallig-wartburg- modelling as LOD is still work in progress. We can, howontology#LES_NARINES">
ever, show a manually created example of entry cantuccino<skos:scopeNote xml:lang="fr">les narines</skos:scopeNote>(LEI 10,1458,32) in List. 6.
<rdfs:label xml:lang="fr">les narines</rdfs:label><rdfs:subClassOf rdf:resource="https://example.org/
1@prefix
lei: <http://www.lei-digitale.org/> .
hallig-wartburg-ontology#HWCat"/><skos-thes:broaderPartitive rdf:resource="https://example.2
3 lei:cantuccino a ontolex:LexicalEntry ;org/hallig-wartburg-ontology#LE_CORPS_ET_LES_MEMBRES"/>4
ontolex:sense lei:cantuccino_sense ;</owl:Class>5
ontolex:evokes lei:cantuccino_lexConcept ;</rdf:RDF>
6
ontolex:canonicalForm lei:cantuccino_form .
Listing 3: Extract of OWL ontology (RDF/XML syntax). 7 lei:cantuccino_form a ontolex:Form ;8
ontolex:writtenRep "cantuccino"@it .
94.

Practical Application

With the use cases of Old Gascon bacon (ham), entry of
DAGél, and of Italian cantuccino (a twice-baked almondbiscuit), entry of LEI, we demonstrate how—through theinterlinking of linguistic resources via the OntoLex-lemon
vocabulary—the integration of a reference to a concept of
the HW ontology can be integrated into an LOD resource.

10
11
12
13
14

Listing 6: Minimal example of LEI data (RDF/Turtle).
HW ontology as cross-mapping hub. The integration ofreferences to the HW ontology is a model to be followedby other resources, where word-sense units refer to thesame HW concepts, thus, installing the HW lightweight
ontology as a cross-mapping hub and an access point tosemantic-driven, language- and word-form independent research. E.g., a database search for the string ‘pâtisserie’within the sense definitions of all DEAFél entries produces 46 results: friolete f. “pâtisserie légère”, fromagie f.
“pâtisserie faite de fromage et d’œufs”, etc. In DAGél, we
find the lexeme habanhas m. “pâtisserie semi-sucrée à basede fèves”.15 A mapping of these lexemes to the corresponding HW concept LE PAIN LA PATISSERIE could thus beintegrated into the LOD versions of DEAF and DAG in
an automated way, leading, in this example, to a semantically driven, extra-linguistic cross-linking of LEI, DAG,
and DEAF.

Old Gascon bacon. The conversion of DAGél dictionary
entries into RDF is an automated process, broadly similar
to the conversion of DEAF (Tittel and Chiarcos, 2018). To
automatically insert a mapping of a sense definition to thecorrect HW concept is straightforward, given that a reference from each sense to HW is part of the XML resource
data, as shown in List. 4.
1
2
3
4<m:definition>viande de porc sal&#xE9;e afin dela conserver</m:definition><m:cat-onomas cat="B I k cc 1">B I k 1 cc 1 /La viande</m:cat-onomas>

Listing 4: XML resource data of a DAGél entry (extract).
The content of the XML element <cat-onomas> can betransformed into hw:LA_VIANDE, to which we can referthrough OntoLex-lemon’s object property isConceptOf,
as shown in List. 5, l. 14.
1
2
3
4

5.

@prefix dag: <http://dag.adw.uni-heidelberg.de/lemme/> .
@prefix hw: <http://example.org/hallig-wartburgontology#> .

7
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11

dag:bacon a ontolex:LexicalEntry ;ontolex:sense dag:bacon_sense ;ontolex:evokes dag:bacon_lexConcept ;ontolex:canonicalForm dag:bacon_form .
dag:bacon_form a ontolex:Form ;ontolex:writtenRep "bacon"@oc-x-40000006 .

14
ISO 639 does not provide a language code for Old Gascon
and we thus follow the pattern to create a unique and decodable
language tag described by Gillis-Webber and Tittel (2020).
15
A search for the HW concept ‘B I k 1 cc 2’ produces 21 lexemes but is less precise, leading also to lexemes denoting flour,
sieving flour, etc.

12
13
14
15
16
17

Discussion and Future WorkIn this paper, we have argued that the modelling of HW
as an LOD resource is an important step towards resource
integration and cross-language accessibility of historicallinguistic resources. The lightweight ontology based on
HW provides a model for external resources, facilitatingreferences for semantic mapping. However, moving from5
6

lei:cantuccino_lexConcept a ontolex:LexicalConcept ;ontolex:isConceptOf hw:LE_PAIN_LA_PATISSERIE ;ontolex:definition "un pezzetto, un ritaglio di pane
dolce mandorlato"@it ;ontolex:lexicalizedSense lei:cantuccino_sense .

dag:bacon_lexConcept a ontolex:LexicalConcept ;ontolex:isConceptOf hw:LA_VIANDE ;ontolex:definition "viande de porc salée afin de laconserver"@fr ;ontolex:lexicalizedSense dag:bacon_sense .

Listing 5: Minimal example of DAGél data (RDF/Turtle).

7

(medieval) historical linguistic resource to an entity of aconceptual model of the modern world difficult: (i) things(abstract or real) denoted by medieval words do not exist
anymore, (ii) words are extinct and, thus, the concepts denoted by them are hard to identify in a modern world ontology, e.g., Old French jaonoi m. “gorse-covered terrain”,
DEAF J 398,30, (iii) meanings of words are extinct, andtheir modern equivalence is not obvious, e.g., Old Frenchjambe f. (“leg”, and also:) “post that serves as a support (fora door lintel, a mantelpiece, a vault, etc.)”, DEAF J 94,15,
and (iv) meanings have undergone semantic shift and theunderlying concept is clearly different from the one of symbolized by the modern corresponding word. E.g., the veine
was considered a sort of blood vessel that transports the‘nourishing blood’ from the liver to each part of the body,and the sperm designated both the male and the female generative cell, etc.17 Hence, a mapping to the modern concepts of ‘veine’ and ‘sperm’ is not possible without causing semantic discrepancies. We refer to this circumstanceas the Historical Semantic Gap. Khan et al. (2014) address the issue of modelling semantic shift with extending
the OntoLex-lemon vocabulary by adding a time interval tocapture different concepts of one lexeme through time. Thisapproach is a major enhancement from the point of view ofhistorical linguistics. However, it does not solve the problem of semantic mapping to an extra-linguistic conceptualmodel where the historical concept is not represented.
To stabilise HW’s role as an onomasiological reference system for historical (linguistic) resources, it must be elaborated in two ways: The net of concepts must be refinedand concepts with historically appropriate content must be
added. We call the latter process the historicisation of HW.
To prepare for a future extension towards historicised content, we foresee a class HistCat and a symmetric (object)
property hasModernCounterpart, cf. List. 7.

the RDF/SKOS format towards an ontology should include adding knowledge that enriches the model throughadditional concepts, relationships, terms, and descriptive
metadata. This means adding labels in other languages,
and scholastic genus–differentia definitions to help grasp
the concepts, e.g., LA VIANDE: “flesh of animals (including fishes and birds and snails) used as food” (useful resources, i.e., dictionaries, WordNet, etc., for thistask need to be evaluated considering conceptualisation
incongruences and translation problems [cp. Bizzoni etal. (2014) on the Ancient Greek WordNet]; a cooperation with MHDBDB seems promising in this regard). Asa first step, we have published the identification scheme
used in Hallig-Wartburg (as shown in List. 3), available athttps://lod.academy/hw-onto/ns/hw#.
Re-engineering the Model into a Formal Ontology. To
enable reasoning over the HW ontology (that is not possible
with the OWL Full model demonstrated above) and to introduce more expressive semantic relations for this purpose
requires the SKOS model to be re-engineered into a formal
ontology. The disjointness condition between OWL classes
and individuals (the SKOS concepts) must hold true forOWL DL, thus, any SKOS and SKOS-THES relations willneed to be removed. However, to align the relationships
expressed through SKOS / SKOS-THES properties withOWL DL is clearly not obvious (Keet and Artale, 2008;
Kless et al., 2012b; Baker et al., 2013; Adams et al., 2015).
It involves finding equivalences for hierarchical whole-part(spatial, structural, etc.) relationships, associative relationships (e.g., action and action instrument / results / participant / target / etc. (Kless et al., 2012b, 422f.), and coining
custom relation properties for relating nuanced same-level
and cross-level relations. Using the re-engineering of the
AGROVOC thesaurus as an example (Baker et al., 2019),
the cost-benefit ratio of a presumably very time-consuming
task must be considered. We thus identify a feasibility analysis of (i) re-assessing the relationships expressed in theoriginal HW resource, (ii) making them explicit and (iii)
expressing them through relations in OWL as future work.
Insufficient Scope and Granularity of HW concepts.
HW shows significant shortcomings that hamper an accurate semantic mapping, reducing its relevance as an extralinguistic cross-mapping hub. The scope and granularity of
HW’s categories do not suffice when modelling the lexicalunits of an entire language: HW is little appropriate for the
mapping of the so-called small words (e.g., pronouns, articles). The differentiation is inadequate: HW is primarily
geared to general language and lacks any kind of technicalprecision, e.g., in fields like ‘L’astronomie’ and ‘La biologie’ that are reduced to one single concept, respectively.

1
2
3
4
5
6
7
8
9
10
11

<owl:Class rdf:about="https://example.org/
hallig-wartburg-ontology#HistCat"><rdfs:label xml:lang="en">historicised concept</rdfs:label></owl:Class>Listing 7: Added property and class to HW ontology.
HW presents few categories that mirror the specificationof historical times: Only four concepts include the notionof ‘ancient’, e.g. ‘Les armes anciennes’ (early weapons,
next to ‘Les armes modernes’) and ‘Les bâtiments de guerre
anciens’ (early warships, next to ‘Les bâtiments de guerre
modernes’). With the added class and object property, e.g.,
the class LES_ARMES_ANCIENNES can be defined a subclass of HistCat and refer to LES_ARMES_MODERNES
through the property hasModernCounterpart. This
would, thus, support the use of HW as an onomasiologicalframework by both historical and modern resources.

Insufficient Possibilities for Depicting Historical Life.
Regional and cultural imprints through time go hand inhand with semantic shift. The HW, like other extralinguistic conceptualisations of the world such as DBpedia,
depicts modern reality. To map Old Italian àghila to HW‘aigle’ or DBpedia ‘Eagle’16 is straightforward. However,with language change and semantic shift, many problems
arise that make the semantic mapping from a lexeme in a16

<owl:SymmetricProperty rdf:about="https://example.org/
hallig-wartburg-ontology#hasModernCounterpart"><rdfs:label xml:lang="en">has modern counterpart</rdfs:label></owl:SymmetricProperty>

17

DEAFpré VEINE1 , https://deaf-server.adw.
uni-heidelberg.de/lemme/veine1;
ESPERME.../lemme/esperme [25-02-2020].

http://dbpedia.org/page/Eagle [10-02-2020].

8

6.

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Proceedings of the 7th Workshop on Linked Data in Linguistics (LDL-2020), pages 11–14
Language Resources and Evaluation Conference (LREC 2020), Marseille, 11–16 May 2020c European Language Resources Association (ELRA), licensed under CC-BY-NC

Transforming the Cologne Digital Sanskrit Dictionaries into Ontolex-Lemon
1

Francisco Mondaca1 , Felix Rau2

Cologne Center for eHumanities - University of Cologne
Data Center for the Humanities - University of Cologne
Albertus-Magnus-Platz, 50923 Cologne, Germany{f.mondaca, f.rau}@uni-koeln.de

2

Abstract
The Cologne Digital Sanskrit Dictionaries (CDSD) is a large collection of complex digitized Sanskrit dictionaries, consisting of overthirty-five works, and is the most prominent collection of Sanskrit dictionaries worldwide. In this paper we evaluate two methods fortransforming the CDSD into Ontolex-Lemon based on a modelling exercise. The first method that we evaluate consists of applying RDFa
to the existent TEI-P5 files. The second method consists of transforming the TEI-encoded dictionaries into new files containing RDF
triples modelled in OntoLex-Lemon. As a result of the modelling exercise we choose the second method: to transform TEI-encoded
lexical data into Ontolex-Lemon by creating new files containing exclusively RDF triples.
Keywords: tei, ontolex-lemon, lexicog, rdfa, sanskrit

1.

Sanskrit Lexicographytionary, (Monier-Williams, 1899) – are highly structured
and complex. These lexicographical microstructures posea challenge to all attempts of developing general schemes
and vocabularies for entry structures and are good test cases
for whether a model can cover complex bilingual, multiwriting system entries.

Sanskrit (ISO 639-3 san) is a classical language from South
Asia. It is the liturgical language of Hinduism and somebranches of Buddhism and was the literary and scientificlanguage of South Asia well into modern times. As a consequence, Sanskrit has a 4000 year long history.
Sanskrit belongs to the Indo-Aryan branch of the IndoEuropean language family and it is the only attested formof Old Indo-Aryan. Based on internal diachronic developments, it is conventionally divided into Vedic Sanskrit(early Old Indo-Aryan, 2000 BCE–600 BCE) and Classical
Sanskrit (later Old Indo-Aryan) after the Vedic period (Masica, 1991).
As the oldest attested form of Indo-Aryan, Sanskrit constitutes one of the oldest attested Indo-European laguages
and is central to our understanding of this language family. From the 19th century onward, philological research
produced a vast array of Sanskrit text editions, grammatical descriptions, and dictionaries. In particular, the Große
Petersburger Wörterbuch (Böhtlingk and Roth, 1855) andMonier-Williams’ Sanskrit-English Dictionary, (MonierWilliams, 1899) are among the most important bilingual
lexicographical works of the 19th century, if not in general.
The corpus of scientific Sanskrit dictionaries consist ofover thirty-five works and includes mono- and bilingual
general dictionaries as well as more specialised thematic
works. Some of these work, such as Grassmann’s dictionary (Grassmann, 1873), are specific to one text. Others arecovering only one specific variety of Sanskrit. For example,
the Buddhist Hybrid Sanskrit Dictionary (Edgerton, 1953)
covers the distinct variety of Sanskrit used in some earlyschools of Buddhism (Burrow, 2001). Other dictionaries
aiming at covering the whole lexical range of 4000 yearsof language history, resulting in lexicographical challenges
modern Sanskrit lexicography still has to address (Lugli,2018).
The entries in the more extensive dictionaries – in particular in the Große Petersburger Wörterbuch (Böhtlingk andRoth, 1855) and Monier-Williams’ Sanskrit-English Dic-

2.
2.1.

About the Cologne Digital Sanskrit
Dictionaries (CDSD)
Overview

The Cologne Digital Sanskrit Dictionaries1 is the mostprominent collection of digitized Sanskrit dictionaries
available on the Internet. This project was initiated in 1994
when XML did not exist yet and Sanskrit had no proper
Unicode support. Sanskrit is traditionally written in a variety of local scripts, but is now generally printed in Devanagari, a the North Indian script that is most prominently used
to write Hindi and Nepali. While support for Devanagari
was already included in Unicode 1.0.0 in 1993, full coverage of the characters needed to encode Sanskrit texts andlexicographic resources was only achieved in 2009 when
the Vedic Extensions were added with Version 5.2. to theUnicode standard. As a consequence, an ASCII-based encoding scheme was developed in 1994 specifically for thiscollection, in order to encode strings in Devanagari, or to
encode its Roman script transliteration (later standardized
as ISO-15919). In 2003, when XML and Unicode werealready part of the technologies available for serializing
language resources, the CDSD offered different web applications for accessing its dictionaries. The CDSD collection consists of more than thirty-five Sanskrit dictionaries,
mostly bilingual dictionaries covering different modern European languages. The CDSD web portal offers different
web applications to access each dictionary. Also from the
CDSD web portal each dictionary can be downloaded. The
1

de

11

https://www.sanskrit-lexicon.uni-koeln.

3.2.

dictionaries can be accessed on GitHub2 in their source format. Their XML-encoded versions can also be accessed via
web APIs3 provided by the API framework Kosh 4 .

2.2.

Searching for Sustainability andInteroperability

During the LAZARUS project (2013-2015)5 in order toprovide a sustainable and interoperable format for the
CDSD collection, a common TEI-P56 schema7 was developed. Three dictionaries were transformed into TEI-P5:
the two most complex dictionaries both from a content anda layout perspective (Monier-Williams, 1899; Böhtlingk
and Roth, 1855) and one English-Sanskrit dictionary (Apte,
1884). During the VedaWeb project (2017-2020)8 , four dictionaries of the CDSD collection (Apte, 1890; Edgerton,
1953; Grassmann, 1873; Macdonell and Keith, 1912) havebeen transformed into TEI-P5 employing the schema developed during the LAZARUS project. VedaWeb offers adigital edition of the Rigveda, the most ancient Indo-Aryan
text. VedaWeb is an API-driven project. On the one hand,the project offers its textual data through a REST API 9 .
On the other hand, the project offers its lexical resources
via REST and GraphQL APIs10 . One of the main features of VedaWeb is that each token of the Rigveda points
to an entry in Grassmann’s dictionary (Grassmann, 1873).
This Sanskrit-German dictionary has been specially compiled for the Rigveda with the goal of defining every token present on it. The VedaWeb app calls the Grassmann
GraphQL API and displays its respective information.

3.

Existing transformation methods

There are two main approaches for transforming TEIencoded data into a Ontolex-Lemon compliant version. The
first approach consists in extracting the lexical data contained in the TEI file and create a new file with this data
modelled in Ontolex-Lemon. This method has been applied
previously to the Dictionnaire étymologique de l’ancien
français (Tittel and Chiarcos, 2018). Tittel and Chiarcos employ XSLT Stylesheets for the transformation. The
same technology is applied at the tei2ontolex GitHub repository11 developed by the European Lexicographic Infrastructure Project (ELEXIS) (Declerck et al., 2019), where
researchers John P. McCrae and Laurent Romary, experts
in Ontolex-Lemon and TEI respectively, have worked together.
To create new files modelled in Ontolex-Lemon would beless verbose, because it would leave the TEI file with its
tags and attributes as it was originally encoded. But thismethod would also duplicate the amount of files containing
lexical data to be curated. It would also require to synchronize both the TEI and Ontolex-Lemon serialized versions.
A second approach consists in employing RDFa within thesource TEI file (Chiarcos and Ionov, 2019), i.e. modelling
TEI and Ontolex-Lemon within the same file. This method
would simplify at first sight the task of encoding the existent lexical data with Ontolex-Lemon. However, an issue
to consider when applying RDFa within TEI files is thatwhile this method is W3C-compliant it is not TEI-endorsed(Chiarcos and Ionov, 2019).
Another issue that arises when modelling digitized dictionaries with Ontolex-Lemon derives from the constraint thatit allows a single part-of-speech (POS) per entry. In thisregard TEI-P5 and TEI-Lex0 12 are flexible because they
allow multiple POS per lexical entry. Consequently, thestructure of a printed dictionary is respected in TEI. On
the contrary, in OntoLex-Lemon the lexicographic structure must be split when an entry contains more than one
POS. This is a negative aspect when modelling and encoding digitized dictionaries because it makes a transformation
scenario more complex and verbose than necessary.
This issue is addressed by the Ontolex-Lemon Lexicography Module (lexicog) 13 . Lexicog does respect the structureof a digitized dictionary. However, it achieves this basicallyfrom a layout perspective. It does create a parallel structure
linked to Ontolex-Lemon core’s module where the lexicographic information, e.g. POS, is encoded. Complexity andverbosity are thus not reduced.
The terms complexity and verbosity are here employedfrom the perspective of a human that reads a file and seeks
to elucidate the model behind it. While XML is the mostverbose of all RDF serializations (Cimiano et al., 2020),
the same applies to Turtle or other RDF serializations whenmodelling digitized dictionaries in Ontolex-Lemon. In our
opinion the complexity and verbosity that emerges whenmodelling digitized dictionaries in Ontolex-Lemon has its

Transforming TEI-encoded dictionaries
into Ontolex-Lemon

3.1. Where to start?
Taking into account the experiences gained during the
projects LAZARUS and VedaWeb, we decided to begin the transformation of the available TEI-P5 dictionaries into OntoLex-Lemon (McCrae et al., 2017) withthe most complex Sanskrit-English dictionary: MonierWilliams (Monier-Williams, 1899). This Sanskrit-English
dictionary is considered to be the most detailed Sanskrit
dictionary compiled in the English language. It is also a
constant reference for Sanskrit scholars. For these reasons
it was chosen to be the basis for creating a TEI-schemathat would be applied to other dictionaries of the collection.
And it will be the basis of this new transformation scenario.
2

https://github.com/sanskrit-lexicon/
csl-orig/tree/master/v02
3
https://cceh.github.io/kosh/docs/
implementations/cdsd.html
4https://kosh.uni-koeln.de5
https://cceh.uni-koeln.de/lazarus
6
https://tei-c.org/guidelines/p5
7
https://github.com/cceh/c-salt_dicts_
schema8
https://vedaweb.uni-koeln.de9https://vedaweb.uni-koeln.de/rigveda/
swagger-ui.html
10https://cceh.github.io/c-salt_sanskrit_
data

11

https://github.com/elexis-eu/tei2ontolex
https://dariah-eric.github.io/
lexicalresources/pages/TEILex0/TEILex0.html
13
https://www.w3.org/2019/09/lexicog
1212

origin in establishing that an entry can have only a single
part-of-speech.

3.3.

Cimiano, P., Chiarcos, C., McCrae, J. P., and Gracia, J.
(2020). Linguistic Linked Data: Representation, Generation and Applications. Springer, Cham, Switzerland.
Declerck, T., McCrae, J., Navigli, R., Zaytseva, K., andWissik, T. (2019). ELEXIS - European Lexicographic
Infrastructure: Contributions to and from the Linguistic
Linked Open Data.
Edgerton, F. (1953). Buddhist Hybrid Sanskrit Grammar
and Dictionary. Yale Univ. Press, New Haven.
Grassmann, H. G. (1873). Worterbuch zum Rig-veda. O.
Harrassowitz, Wiesbaden.
Lugli, L. (2018). Drifting in Timeless Polysemy: Problems of Chronology in Sanskrit Lexicography. Dictionaries: Journal of the Dictionary Society of North America, 39(1):105–129, August.
Macdonell, A. A. and Keith, A. B. (1912). Vedic Index ofNames and Subjects. J. Murray, London.
Masica, C. (1991). The Indo-Aryan Languages. Cambridge University Press, Cambridge.
McCrae, J. P., Bosque-Gil, J., Gracia, J., Buitelaar, P., andCimiano, P. (2017). The OntoLex-Lemon Model: Development and Applications. In Electronic lexicography
in the 21st century. Proceedings of eLex 2017 conference.
Monier-Williams, M. (1899). A Sanskrit-English dictionary. The Clarendon Press, Oxford.
Tittel, S. and Chiarcos, C. (2018). Linked open data forthe historical lexicography of old french. In Proceedings of the Eleventh International Conference on Language Resources and Evaluation (LREC 2018), Paris,
France, may. European Language Resources Association(ELRA).

Modelling Ontolex-Lemon with RDFa

As seen in the previous section the two presented methods
for converting TEI-encoded data into Ontolex-Lemon consist in creating new files containing exclusively RDF triples
or in applying RDFa within the same TEI file.
Figure 2 shows an entry in Monier Williams that has been
partially modelled in RDFa using Ontolex-Lemon. Figure 1
shows the entry as it has been modelled in TEI-P5. The firstproblem that arises in figure 2 is that morphosyntactic information encoded as part-of-speech (POS) in Ontolex-Lemon
can not be related to an ontolex:LexicalSense. This
must be related to an ontolex:LexicalEntry. A possible solution for applying RDFa would be to modify theexistent XML-TEI structure, i.e. create new XML nodes.
Another issue when applying RDFa and Ontolex-Lemon
relates to choose an external ontology or vocabulary
for encoding POS. When encoding lexical data with
Ontolex-Lemon, usually the lexinfo14 ontology is employed. In figure 2, the POS ‘mfn.’ means ‘masculine,
feminine and neutral’. There is no such category inlexinfo:partOfSpeech for this POS. A possible solution would be to create a vocabulary containing the POS
to be found in all the Sanskrit dictionaries of the collection
and later map the values of this vocabulary to existing values of lexinfo:partOfSpeech.

4.

Conclusion

At first sight RDFa seemed to tackle our requirements better than creating new files containing RDF triples whentransforming TEI source files into Ontolex-Lemon. But
a brief modelling exercise showed that employing RDFa
required adding new elements into the TEI source files.
These structural modifications to the TEI files would unnecessarily complicate the maintenance of these files overtime. Therefore, the method to follow will be to create completely new files modelled in Ontolex-Lemon. To this end,
we will follow the experiences made during the transformation of the Dictionnaire étymologique de l’ancien français(Tittel and Chiarcos, 2018) into Ontolex-Lemon, as well as
the current development of the tei2ontolex15 repository.

5.

Bibliographical References

Apte, V. S. (1884). The Student’s English-Sanskrit Dictionary. Arya Bhushana, Poona.
Apte, V. S. (1890). The Practical Sanskrit-English Dictionary. Shiralkar, Poona.
Burrow, T. (2001). The Sanskrit Language. Motilal Banarsidass Publ.
Böhtlingk, O. and Roth, R. (1855). Sanskrit Wörterbuch.
Herausgegeben von der kaiserlichen Akademie der Wissenschaften, bearbeitet von Otto Böhtlingk und Rudolph
Roth. Eggers, St-Petersburg.
Chiarcos, C. and Ionov, M. (2019). Linking the TEI. Approaches, Limitations, Use Cases. Digital Humanities
2019, July.
14
15

https://lexinfo.net
https://github.com/elexis-eu/tei2ontolex

13

Figure 1: Entry ’aSrata’ in Monier Williams modelled in TEI-P5

Figure 2: Entry ’aSrata’ in Monier Williams modelled in TEI-P5 and partially with errors in Ontolex-Lemon with RDFa

14

Proceedings of the 7th Workshop on Linked Data in Linguistics (LDL-2020), pages 15–22
Language Resources and Evaluation Conference (LREC 2020), Marseille, 11–16 May 2020c European Language Resources Association (ELRA), licensed under CC-BY-NC

Representing Temporal Information in Lexical Linked Data Resources
Anas Fahad Khan

CNR-Istituto di Linguistica Computazionale “A. Zampolli"
Pisa, Italy
fahad.khan@ilc.cnr.it
Abstract
The increasing recognition of the utility of Linked Data as a means of publishing lexical resources has helped to underline the needfor RDF-based data models with the flexibility and expressivity to be able to represent the most salient kinds of information containedin such resources as structured data; this includes, notably, information relating to time and the temporal dimension. In this article
we describe a perdurantist approach to modelling diachronic lexical information which builds upon work which we have previously
presented and which is based on the ontolex-lemon vocabulary. We present two extended examples, one taken from the Oxford English
Dictionary, the other from a work on etymology, to show how our approach can handle different kinds of temporal information often
found in lexical resources.
Keywords: linguistic linked data, diachronic lexical data, perdurantism

1.

Introduction

information contained in such resources as structured data:
this includes, notably, information relating to time and thetemporal dimension. Time is a central concern of certain
kinds of lexical resource, this is most obviously true of etymological and historical dictionaries, but the inclusion of
temporal information in lexical resources is by no means
limited to such specialist works, and etymologies in particular are found in a wide range of dictionaries and lexical datasets. In previous related work we have looked at
how to represent etymologies, viewed as hypotheses about
word histories, explictly both in RDF (Khan, 2018) and in
the Lexical Markup Framework (Khan and Bowers, 2020).
However, temporal information is not always explicitly included in the form of an etymology. For instance, it is also
common for resources to list the senses in each lexical entry
in some order of temporal precedence2 . Other resources include descriptions of the semantic shift processes which led
from temporally antecedent senses to subsequent senses;
yet others mark senses (or forms, etc) as obsolete and/or
give some basic information on the time period in which
a sense or form (or grammatical construct) was in use or
was most commonly in use. In fact, in these and in othercontexts, one frequently finds reference to a particular historical stage of a language, such as Old French or Middle
English, something which also helps to group together lexical phenomena in time. Many lexical resources, especiallymore authoritative or scholarly dictionaries, also give citations for separate senses and even of forms (or, in fact,
for any interesting or salient lexical information which haschanged over time, such as verb transitivity or the historic
existence of now obsolete noun declensions). These citations help to locate senses (and forms etc) in time, and oneof the central aims of the current work is to show how suchinformation can be efficiently integrated into the RDF encoding of an entry. It is worth noting that the information
originating from citations also tends to be less vague thanThe difficulties of representing relationships that changewith time – also referred to as fluents or diachronic relations in the literature – in RDF have been by now wellrehearsed (Welty et al., 2006). The core problem here, ofcourse, is that the RDF framework does not allow us to simply add an extra temporal parameter to binary and unary
properties: something that would otherwise make modelling diachronic relations fairly straightforward. A number of different ‘workarounds’ have been proposed to deal
with this situation1 . There is, however, no single one size
fits all solution that will work in every case and different
solutions are better suited to different use cases. In thisarticle our focus will be on lexical data, and in particular
data that derives from legacy resources including dictionaries and scholarly works on meaning change. In the courseof the article we will look at some of the different ways inwhich such data can carry a temporal dimension, including
indirectly, through the use of citations and attestations. We
will then propose the use of a perdurantist design pattern
for representing this temporal information. Our intention
with this submission is to rouse interest in the perdurantist
approach to modelling lexical change in light of the work
which is going on both in the W3C Ontolex community andin a number of projects and use cases which have recently
arisen, and to elicit feedback from the Linguistic Linked
Data community in order to help determine the variety ofuse cases which the approach is able to handle well andthose which it cannot.

2.

Temporal Information in Lexical Datasets

The increasing recognition of the utility of Linked Data asa means of publishing lexical resources – thanks in largepart to projects such as ELEXIS (Krek et al., 2018) and
LiLA (Passarotti et al., 2019) – has helped to underline theneed for RDF-based data models with the flexibility and expressivity to be able to represent the most salient kinds of2

For instance the Oxford English Dictionary describes its
entries as being "structured to show the evolution of senses
and uses over time". cite https://public.oed.com/
how-to-use-the-oed/glossary/

1
See
for
instancehttps://www.w3.org/TR/
swbp-n-aryRelations/15other kinds of temporal data present in lexical resources, as
will be illustrated by the examples which we will present
below. It is a hallmark of most lexical temporal data thatit tends to be vague, sometimes very vague (for instance inthe case of proto-languages with no written testimony). In
the majority of cases it is hard to pinpoint the year, or eventhe century, that a certain sense or form or word began to
be used and/or stopped being used. In other cases it is hard
to fix the historical periods in which entire languages werespoken. If we are going to potentially reason with such data
we need an approach that takes into consideration the vague
nature of such data. We discuss this further and propose asolution in Section 3.1..

2.1.

groups. The first group is labelled as being "[s]enses relating to a person", and the second group (containing only
two senses) is labelled as "other senses". Finally a list ofphrases and derivatives is presented. Note that the senses
are listed in historical order, although in some cases a sense
may have a more recent subsense listed immediately below
it and before other historically later senses. The forms arelisted as follows in the entry:
ME garl, ME geerl, ME gerl, ME (18– chiefly
Irish English and nonstandard) gurl , ME–15
gerle, ME– 16 girle, ME–16 gyrle, 15 gierle, 15
gurle, 15 gyrll, 15–16 guirle, 15–16 gyrl, 15–girl, 16 garle, 16 gerreld; Caribbean 19– gyal,
19– gyul.

Previous Work

The work in this paper builds upon, and in many cases
presents in altered form, ideas and proposals which we havepublished previously. We initially presented the idea of extending the lemon (McCrae et al., 2011) and afterwards the
ontolex lemon (McCrae et al., 2017) models using perdurants in order to represent temporal information in (Khan etal., 2014) and (Khan et al., 2016), using the resulting model
to encode a linguistic dataset dealing with the evolution ofemotion terms in Old English in (Khan et al., 2018)3 . Manyof the properties and classes in that version have been modified as a result of working on modelling various different
lexical datasets. We have also worked on the more specific case of modelling etymologies as linked data (Khan,
2018). However the current work focuses much more on
the representation of time and temporal intervals than thoseprevious works, developing our approach by focusing on
two extended examples.

Here ME stands for Middle English, a time period which
the OED describes as running from 1150 CE to 1500 CE4 .
The numbers 15, 16, 18, and 19 refer to the 1500s (i.e.,
1500-99)5 , the 1600s (i.e., 1600-99), etc. The senses arelisted under numbers and, in the case of subsenses, lower
case roman letters. Each sense starts with a definition andsome other related information before presenting a list of
historical citations for that sense. Below we give the first
sense in full:
1. Chiefly in plural. A child of either sex; ayoung person. Now Irish English (Wexford).
knave girl n. a boy.
C1300 St. Thomas Becket (Laud) 76 in C.
Horstmann Early S.-Eng. Legendary(1887) 108 (MED) Be Amirales dou`ter
was In Be strete Bare-oute, And suyBegret prece of gurles and Men comen hireal-a-boute.
C1400 (→a1376) W. LANGLAND Piers
Plowman (Trin. Cambr. R.3.14) (1960)
A. XI. 132 (MED) Gramer for girles[v.rr. gurles, gerles, childeryn] I garte
ferst write, And bet hem wiB a Baleis but`if Bei wolde lerne.
C1400 (→?a1300)
Kyng
Alisaunder(Laud) (1952) 2798 (MED) Men mi`tten
seen Bere hondes wrynge..Wymmen
shrikyng, gyrles gradyng.
C1405 (→c1387–95) G. CHAUCER Canterbury Tales Prol. (Hengwrt) (2003) l.
664 In daunger hadde he at his owene
gyse The yonge gerles of the diocise,
And knew hir conseil, and was al hirreed.
a1475 Bk. Curtasye (Sloane 1986) l. 328
in Babees Bk. (2002) I. 308 Ne delf Bou
neuer nose thyrle With thombe ne fyngur, as `ong gyrle.

2.2. Case Studies
2.2.1. The word girlFor our first case study we will look at the Oxford English
Dictionary (OED) entry for the word girl (OUP, 2008). We
chose the OED because of its status as an authoritative workof descriptive, historical lexicography and because of thecomprehensive, and therefore challenging (from the modelling point of view), nature of its entries. The OED epitomizes the type of the historical reference dictionary inwhich individual forms and senses are attested with reference to a historical corpus of citations. In the current case
we have chosen the word girl because of the somewhat surprising fact (regularly cited in books on etymology, at leastin the English language), that it was originally used to referto a "child of either sex; young person" and not just, as is often the case today, to "a young or relatively young woman".
The OED entry starts by giving the standard pronunciationof girl in IPA in both British and American English. Next alist of forms is given, we will come back to this shortly. Following this, frequency, origin and etymological information
is listed (the etymology of the word is especially obscureand its origin is given as unknown). Then twelve separate
senses for the word are given; these are classified into two

4

https://public.oed.com/blog/
middle-english-an-overview/5
https://public.oed.
com/how-to-use-the-oed/
key-to-symbols-and-other-conventions/3

Ours was not of course the first attempt to model diachronic
lexical or etymological information in linked data. See for instance (Moran and Bruemmer, 2013), (De Melo, 2014). (Khan,
2018) presents a fuller overview of related work.

16

?a1475 Ludus Coventriae (1922) 171(MED) Here knaue gerlys I xal steke.

possess. In this case we can make use of Allen relations
between temporal intervals (Allen, 1983) in order to reason over such data, and fortunately for us these relations
are already encoded within the popular temporal ontology
OWL-time7 , see Figure 1. Furthermore there already exists
a set of SWRL rules which allow for reasoning over datathat describes intervals qualitatively using these relations(Batsakis et al., 2017).

a1827 J. POOLE Gloss. in T. P. Dolan &D. Ó . Muirithe Dial. Forth & Bargy(1996) 49 Gurl, gurlès, a child, a girl.
1996 T. P. DOLAN & D. Ó. MUIRITHE
Dial. Forth & Bargy 25 Gurl, a childof either sex.
Note that the C before a date means ‘circa’, approximately,
and a means ‘antes’, before or prior to. The question markindicates an uncertain date. In cases where there are twodates, one after the other, with the second in parenthesis
following an arrow symbol, e.g.,C1400 (→a1376), the firstdate refers to the dating of a manuscript, and the second,the date of composition6 .

2.3.

Sad

The next example which we will model is adapted fromthe etymology for the word sad given in Philip Durkin’s
Oxford Guide to Etymology (Durkin, 2009). We have chosen this example in order to illustrate how to use our approach to model historical sense shifts (although it can beeasily adapted to show the evolution of forms as well for instance). The example regards the meaning shift undergone
by the English word sad which originally meant ‘satisfied’or ‘full’ in Old English and now has the principal meaning of ‘sorrowful, mournful’ (this meaning is recorded, as
Durkin points out, in a source dated a1300). Durkin hypothesises a process of semantic shift that takes place inthree stages, via an intermediate sense meaning ‘weary or
tired of something’, as follows:

Figure 1: Allen relations used in OWL-time.

satisfied, having had one’s fill (of something)
As well as defining, or (in cases when they already exist) reusing, standard temporal intervals such as the 13th Century
CE or 1066 with OWL-time8 we can also define language
specific intervals such as Middle-English whose definitions
are much more subject to variation across different sources:in our case, for Middle-English, we use the definition givenby the OED of (1150, 1500). So, for instance, we can define the 14th century, or rather the 1300s as, running from
1300-1399 using the OWL-time vocabulary as follows.

[metaphorized and narrowed] > weary or tired (ofsomething)[broadened] > sorrowful, mournful.

3.

Our Approach

In this section we will outline and motivate our particularapproach to modelling diachronic lexical data in RDF. The
idea, in a nutshell, is twofold: firstly, we propose the use ofqualitative intervals to model temporal vagueness in lexical
data; then, secondly, we define a ‘perdurantist’ version ofcertain classes in the ontolex-lemon model in order to allow
lexical entries, senses, etc, to each have a ‘lifespan’ as wellas temporal parts.

3.1.

Listing 1: Definition of 13:13 a owl-time:Interval ;owl-time:hasBeginning [a owl-time:Instant ;owl-time:inXSDDateTimeStamp"1300-01-01T00:00:00Z"^^xsd:dateTimeStamp];
owl-time:hasEnd [a owl-time:Instant ;owl-time:inXSDDateTimeStamp"1399-12-31T23:59:59Z"^^xsd:dateTimeStamp] ;owl-time:intervalMeets :14 ;owl-time:intervalMetBy :12 ;rdfs:label "(1300-1399)"@en.

Introduction to Qualitative Temporal
Intervals and Allen Relations

One way of dealing with the kinds of temporal vagueness
which we have previously mentioned is to work with socalled qualitative constraints and to focus on the relative
temporal positions of different points and intervals on agiven timeline, that is, in addition to leveraging whateverexact quantitative information that we might also actually

7

https://www.w3.org/TR/owl-time/
We are using OWL-time but any other temporal vocabulary/ontology with similarly defined properties and classes canalso be used.
8

6

https://public.oed.com/blog/
dating-middle-english-evidence-in-the-oed/17

Note that we have defined the interval both in terms of its
beginning and end points (using the xsd:dateTimeStamp
datatype property) as well as by relating it to two other intervals using the object properties intervalMeets and intervalMetBy. In addition we can also define the time interval, ME, mentioned in the OED entry and corresponding to
the time interval in which Middle English was spoken, by
specifying its start and end years and its relationships withother (named) intervals. So in this case we state that it overlaps the interval 11 (the 10th century), contains the interval
12 (the 11th century), and is finished by the interval 13.

essential thing, however, is to pick a modelling approachand to remain with it consistently throughout a dataset. For
instance the option which we have chosen is to model Cd,
where d is a date, as being contained in an interval of 10
years before and after d.
Listing 6: Modelling circa:circa_1300 a owl-time:Interval ;owl-time:intervalContains [a owl-time:Interval ;owl-time:hasBeginning [a owl-time:Instant ;owl-time:inXSDDateTimeStamp"1290-01-01T00:00:00Z"^^xsd:dateTimeStamp ];owl-time:hasEnd [a owl-time:Instant ;owl-time:inXSDDateTimeStamp"1310-01-01T00:00:00Z"^^xsd:dateTimeStamp ];].

Listing 2: Definition of the Middle English interval:enm_interval a owl-time:Interval ;owl-time:hasBeginning[ a owl-time:Instant ;owl-time:inXSDDateTimeStamp"1150-01-01T00:00:00Z"^^xsd:dateTimeStamp] ;owl-time:hasEnd[ a owl-time:Instant ;owl-time:inXSDDateTimeStamp"1399-12-31T23:59:59Z"^^xsd:dateTimeStamp] ;rdfs:label "Middle English (1150-1300)"@en ;owl-time:intervalOverlappedBy :11 ;owl-time:intervalContains :12 ;owl-time:intervalFinishedBy :13 .

It will also be useful to define an interval EnglishInterval corresponding to the time during which the English
language was spoken and which is ended by the presentand ‘contains’ other, previously defined, intervals such as
enm_interval; every English lexical entry in the resource
can then be interval contained by EnglishInterval. We canfurther temporally locate this interval by adding statements
to the effect that English language was spoken after protoGermanic, while also taking into account an intervening
proto-English period by defining the appropriate intervalBefore Allen relation.

We can, on the basis of these prior ‘building block’ intervals, once again use Allen relations to define further interval combinations and thereby capture other salient intervals
such as, for instance, the interval ME-16 mentioned in thelist of historical forms given above for girl.

4.

Perdurantism v. Endurantism

Although the perdurantist approach has recently become
popular amongst computer scientists and knowledge engineers for reasons that are in large part practical, it has
its basis in a well established philosophical theory. Perdurantism, also known as four dimensionalism (in its ismost common formulations), argues that time should betreated analogously to the spatial dimensions so that objects
can have temporal extension just as they can have length,
breadth, and width: this means that they can have (spatio)temporal parts in the same way that we usually describe
them as having spatial parts. Naturally the notion of temporal part is fundamental to the perdurantist approach; wewill use the following definition, given by (Sider, 1997).

Listing 3: Definition of ME-16:ME-16 a owl-time:Interval;
owl-time:intervalContains :12 , :13 ,:14 , :15 ;owl-time:intervalFinishedBy :16 ;owl-time:intervalStartedBy :enm_interval .

The case of the interval 18- above is a little bit trickier inthat it requires the specification of one point as the present.
Here we have chosen a point in the current year (although
the point in question could be the date of the publication of
the resource being modelled for instance).
Listing 4: Setting a present point:present a owl-time:Instant;
owl-time:inDateTime [a owl-time:DateTimeDescription ;owl-time:unitType owl-time:unitYear ;owl-time:year "2020"^^xsd:gYear ;] ;rdfs:comment "The Present"@en .

x is a temporal part of y during interval T =df(i) x is a part of y at every moment during T ; (ii)x exists during T , but only during T ; and for any
sub-interval t of T , x overlaps every part of y att.

Assuming then that we have already defined the intervals 18, 19, and 20, we can define 18- as ininterval that is started by the 17th century, contains
the 19th century and is overlapped by the 21st century.

Perdurantism by treating people, animals, and things in
general like processes, only parts of which exist at different
times, has the clear shortcoming that it can be unintuitiveand difficult to understand. For a perdurantist, the temporalpart of a person that perdured (to use the technical term)
through, say, the month of January, is a part of them in thesame or in an exactly analogous way as their limbs or theirorgans, things which constitute a physical part of them.
However, the vast majority of people simply don’t thinkin this way about the world. Perdurantism constrasts withthe philosophical approach known as endurantism, or three
dimensionalism, and which many philosophers argue is a

Listing 5: Modelling 18:18- a owl-time:Interval ;owl-time:hasEnd :present ;owl-time:intervalContains :19 ;owl-time:intervalStartedBy :18 ;owl-time:intervalOverlaps :20 ;rdfs:label "(1800-)"@en .

One of the biggest challenges in the present context
relates to the use of the preposition ‘circa’ as in C1300 or
C1400, which obviously is used to codify ‘fuzziness’. The

18

periods, Italyt1,Italyt2 and Italyt3 where: temporalExtent(Italyt1,t1),temporalExtent(Italyt2,t2),
and temporalExtent(Italyt3,t3)

more natural way of thinking about existence through time.
Indeed, according to most endurantist acccounts, things,
objects, etc, instead of being only partially present at one
single point of time, are wholly present at each instant of
their existence9 (Sider, 1997). The perdurantist approach,however, has other features which compensate for its relative conceptual oddity. Most importantly, it helps to resolve a number of longstanding metaphysical conundrums
relating to change over time. In addition, it provides a very
useful way of modelling vagueness, and is also able to meetseveral of the challenges raised against more traditional theories of time and change by the theory of special relativity(Effingham, 2012). Note that although the names ‘threedimensionalism’ and ‘four-dimensionalism’ might suggestthat we are only dealing with ‘concrete’ objects, namely
those that occupy a continuous physical portion of space,this is not in fact the case and perdurantism has in fact been
applied to musical works (Caplan and Matheson, 2006) andinstitutional objects (Hansson Wahlberg, 2014). Our proposal in this work is to apply it to language and linguistic
phenomena. We can explain the precise kind of perdurantist
approach which we take in this paper through the provisionof a simple (non-linguistic) example.
The relation capitalCity, which links together an urban location with a state, can be modelled as a diachronic relation
or fluent since it can change over time, i.e., a country canhave different capital cities at different points in time. Indeed this is the case with the nation of Italy which has had
three separate capital cities, or seats of government, since
its unification in 1861. These were/are: Turin from 1861 to1865 (period t1), Florence from 1865 to 1871 (the periodt2), and Rome from 1871 to the current day (the intervalt3). One perdurantist approach to modelling this situation(and indeed the one which we will propose for lexical data
below) is the following:• Finally
werelatethesetimeslices
of
Italy using the capitalCity relation:
capitalCity(TurinCapital,Italyt1),
capitalCity(FlorenceCapital,Italyt2),
capitalCity(RomeCapital,Italyt3).
This is a version of the kind of perdurantist approach initially proposed for RDF in (Welty et al., 2006); our version is more directly based on (Krieger, 2014). However itshould be noted that when it comes to creating diachronic
versions of relationships between a lexical entry and its lexical senses and forms we can make a simplification. Theselatter are usually defined as being dependent on lexical entries, not least in the ontolex-lemon model where a form ora lexical sense cannot be shared by more than one entry.
That is although the relation sense which relates a lexical entry together with each of its senses is a diachronic
relation, each of the senses in question is parasitic on thatentry, that is, the lifespan of a sense is necessarily containedin the lifespan of an entry, and similarly with forms. Our
proposal then is, when it comes to perdurants versions ofthese classes, to define sense as holding between lexical
entries and senses (and form as holding between lexical entries and forms) rather than timeslice of these, as in the caseof capitalCity.

4.1.

First Definitions

Our first definitions are, as anticipated, perdurantist subclasses of the ontolex-lemon classes Lexical Entry, Lexical Sense, and Lexical Form; these are pLexical Entry,
pLexical Sense, and pLexical Form respectively.
pLexical Entry v Lexical Entry• We define separate time slices (or temporal parts) TurinCapital, FlorenceCapital, RomeCapital of eachof the Italian cities mentioned above, Turin, Florence,Rome respectively. Note that each of these timeslices are also typed as cities, e.g., city(Turin) andcity(TurinCapital)

pLexical Sense v Lexical Sense
pLexical Form v Lexical Form
In order to define these classes we will make use of the new
object property mentioned above, temporalExtent, whose
range is the owl-time class time:Interval, and whose purpose is to relate a perdurant to its temporal dimension;
thereafter we impose the restriction that each member ofthe p- classes is related to exactly one time:Interval individual via the property temporalExtent.

• We relate the temporal parts of these cities together with their wholes using the property temporalPartOfwhich relates a perdurant together withanother perdurant of which it is a temporal part,i.e., temporalPartOf(TurinCapital, Turin), temporalPartOf(FlorenceCapital, Florence), and temporalPartOf(RomeCapital, Rome)pLexical Entry, pLexical Sense, pLexical Form

• Each of these timeslices is associated with its lifespan using the temporalExtent property which relates a perdurant together with the interval during
which it exists, i.e., temporalExtent(TurinCapital,t1), temporalExtent(FlorenceCapital, t2), temporalExtent(RomeCapital, t3).

v = 1 temporalExtent.(time:TemporalEntity)∃temporalExtent.> v time:Interval
4.1.1. Modelling the girl Example
In this section we will model the OED entry for girl using the classes and properties just defined. Note that forreasons of space and clarity of explanation we will focus
on those parts of the entry that show the use of our newclasses and properties rather than giving a comprehensive
encoding of the entry using elements already available in• We also create time slices of Italy for each of these9
T he standard approach to ontology modelling and knowledge
engineering can be described as endurantist.

19the ontolex-lemon module and the recently published lexicographic extension of the latter10 (Bosque-Gil et al., 2017)(so for instance we leave out the canonical form and phonetic forms below).
We start by defining the entry as a pLexicalEntry with an associated temporal extent (girl_time)and relating the entry with its forms using the(non-fluent)
ontolex-lemon
property
lexicalForm

encode the adverb chiefly in the description of the form
gerl? It would be tricky to come up with a general wayof encoding such descriptions that would please everyone).
However this also leaves the door open to encoding suchinformation in other ways and using other properties, forinstance when it comes to purely geographical variations (aspecific case which we plan to look at in future work). In
Listing 9, therefore, we model the temporal interval associated with gurl_form as consisting of two separate temporal
part: relating it to the interval enm_interval using intervalStartedBy and the interval 18- using intervalFinishedBy.

Listing 7: The entry girl and its forms.
:girl rdf:type :pLexicalEntry ;:temporalExtent :girl_time;
ontolex:lexicalForm :garl_form ,:garle_form ,:geerl_form ,:gerl_form ,:gerle_form ,:gerreld_form ,:gierle_form ,:girle_form ,:guirle_form ,:gurl_form ,:gurle_form ,:gyal_form ,:gyrl_form ,:gyrle_form ,:gyrll_form ,:gyul_form .

Listing 9: gurl
gurl_form a :pForm ;:hasTemporalPart :gurl_form_IEN , :gurl_form_ME ;:temporalExtent [a owl-time:Interval;
owl-time:intervalStartedBy :enm_interval;
owl-time:intervalFinishedBy :18-;];
ontolex:writtenRep "gurl"@en .
:gurl_form_ME a :pForm ;:temporalExtent :enm_interval .
:gurl_form_IEN a :pForm ;:temporalExtent :18- ;:hasUsageNote"chiefly Irish English and nonstandard"@en .

Note that each form is contained within the temporal extent
of the entire entry girl_time.
The first two forms which we will look at from the list
given in Listing 7 are garl and girle, the first of which
the OED tells us was in use during the Middle English
period and the second of which was in use from theMiddle English period through to the 15th century. We
model these two as in Listing 8: that is after we introduce them as elements of type pForm we thereafter associate them with a given temporal interval using the property temporalExtent. In this case the two periods arethose defined above, namely emn_interval and ME-16.

Now we will move onto the temporal modelling of the information contained in the senses of the entry11 . We canuse this information to delimit the temporal interval associated with the sense in time. In what follows we focus
on the first sense of the word, the one with the definition‘[a] child of either sex; a young person. Now Irish English(Wexford).’ This usage of the word is attested in texts suchas Piers Plowman and the Canterbury Tales and the 13th
century text Ludus Conventriae. The entry for this sense
also identifies a temporal part of this sense with a particular quality not shared by the whole: that of being limited
to a certain geographically defined dialect (Irish English,
or more precisely the dialect of Wexford). And in fact thissense continues to be used up till the present day. We canmodel this as in Listing 10.

Listing 8: The forms garl and girle:garl_form rdf:type :pForm ;ontolex:writtenRep "garl";:temporalExtent :enm_interval .
:girle_form rdf:type :pForm ;ontolex:writtenRep "girle";:temporalExtent :ME-16 .

Listing 10: First gurl sense:girl ontolex:sense :sense_I1 ,:sense_I1_irish_english .

In the case of the forms gurl, gyal, and gyul we have extra dialectal and geographical information to take into consideration, that is alongside the purely temporal information which has been provided. Indeed the form gurl has
two separate temporal parts. The first part perdures over
the ME period; the second part, which perdures over aninterval which starts in the 18th century and ends in thepresent, is described as being "chiefly Irish English andnonstandard"). In order to model cases such as the latter,
we have added a new datatype property hasUsageNote
to our model; this allows for the encoding of geographical and dialectal constraints on the usage of a lexical element as free text. Our choice in this regard was informedby the fact that such information is often difficult to encode as structured data using formalisms such as RDFS
and OWL (for instance in the present case how would we
10:sense_I1 a :pLexicalSense ;:hasTemporalPart :sense_I1_irish_english ;:temporalExtent :sense_I1_interval .
:sense_I1_irish_english :pLexicalSense ;:temporalExtent :sense_I1_irish_english_interval ;:hasUsageNote "Irish English (Wexford)"@en .

This brings us to the question of how to integrate the temporal information included in the illustrative examples in the
OED entry. Note that in this article we will not discuss how
to model the bibliographic information included with eachillustrative example – something which we can do using
pre-existing vocabularies as well as potentially new classes
and properties – even though it would be (scientifically)valuable to have this kind of information in a structured
11

Note that whenever we can assume that the ordering of senses
is given in temporal order we can define precedence relations between them using, you guessed it, Allen relations.

https://www.w3.org/2019/09/lexicog/

20form; this is purely for reasons of space but it is something
we hope to address in further work. Instead we will use thetemporal information included in the examples to enrich thedescription of the intervals associated with each sense.
In terms of the illustrative examples given in the entry wetake those that allow us to give some kind of a (vague) temporal outline to the use of that sense. We therefore utilize
the periods C1300, a1475 and 1996 for the main sense andthe two periods/intervals12 a1827 and 1996 for the second
Irish English interval. Putting everything together we candefine the two intervals in question as in Listing 11.

to model the fact that one sense ‘gives birth to’ another. We
use the following property chain to relate this to the properties mentioned previously:
shiftSource o shiftTarget .
We define the sad entry as having three separate senses
Listing 12: The entry for sad:sad a :pLexicalEntry ;:temporalExtent [a owl-time:Interval ;:intervalFinishes :EnglishInterval ];ontolex:sense:sad_sense_1 , :sad_sense_2 , :sad_sense_3 .

Listing 11: First girl sense intervals.

Moreover we also define two shift objects relating together the second and third senses of the words. (Wehave used rdfs:comment here to describe each shift
however we are currently working on developing ataxonomy of semantic shifts which we will present
in future work and to which we can link such shifts).

:sense_I1_interval a owl-time:Interval ;owl-time:hasEnd :present ;owl-time:intervalFinishedBy:sense_I1_irish_english_interval;
owl-time:intervalContains:a1475 , :circa_1300 , :year_1996 .
:sense_I1_irish_english_interval a owl-time:Interval;
owl-time:intervalFinishes :sense_I1_interval ;owl-time:intervalContains :a182 .

Listing 13: Two semantic shifts for the senses of sad:sad_shift_1 a :SemanticShift ;:shiftSource :sad_sense_1 ;:shiftTarget :sad_sense_2 ;rdfs:comment "metaphorized and narrowed"@en .

Another way of integrating this information into the entry
is to view each of the illustrative examples as describing avery restricted sub-sense of the main sense (the main sensebeing in this case ‘A child of either sex; a young person’):indeed the sub-sense of the main sense restricted to thatparticular use of the word. This allows us to create a new
pSense for each example to which we can attach all the temporal information included in the illustrative example, andwhich can then be related to the main sense using the appropriate Allen relations. Finally it is important to note thata word or any lexical entry will always have some minimal
temporal information associated with it thanks to the factthat it belongs to a language or a language stage to which
we can usually associate a minimal of temporal information.

4.2.

:sad_shift_2 a :SemanticShift ;:shiftSource :sad_sense_2 ;:shiftTarget :sad_sense_3 ;rdfs:comment"broadened"@en .

The three senses of sad which we have singled out (andwhich are of course far from being exhaustive for the word
can be defined as follows.
Listing 14: The senses of sad:sad_sense_1 a :pLexicalSense ;:senseShiftsTo :sad_sense_2 .
:sad_sense_2 a :pLexicalSense ;:senseShiftsTo :sad_sense_3 .

Modelling semantic shifts: the sad example.

:sad_sense_3 a :pLexicalSense;:temporalExtent [a owl-time:Interval ;:intervalDuring :a1300 ].

The sad example given in Section 4.1.1. could be modelled
explicitly using etymologies (and indeed the more extended
history of the word presented in (Durkin, 2009) would bebetter modelled this way). However in cases where we want
to track and describe a change in the sense of one word or
the phonological changes in the pronunciation of a single
form, it is often more convenient and efficient not to represent this via an etymology. Summarising the problem thenour task is, given two lexical senses l_1, l_2 which have undergone a process of semantic shift of type s, to model thetyped sense shift relationship semanticShift between them:5.

In this article we have attempted to consolidate and build
upon work which we have previously introduced, with theaim, this time, of making a detailed and convincing casefor the expressive advantages of the perdurant approach
to modelling diachronic lexical information. A secondary,though related aim has been to demonstrate how naturally
certain, common, kinds of temporally-enriched dictionary
data can be modelled in this way. In future work, as we havementioned above, we aim to integrate bibliographic information more generally, making use of vocabularies such as
CiTO as well as making use of the new Frequency Corpus
and Attestations extension of ontolex lemon currently being developed by the W3C ontolex group. We also plan toinvestigate the efficiency of temporal reasoning using the
perdurantist approach along with vocabularies like OWLtime and the rules developed by (Batsakis et al., 2017) andto test it on different sizes of dataset; something which we
had previously begun to do in (Khan et al., 2018). We arealso planning, with colleagues, to undertake a more detailed

semanticShift (l_1, l_2, s)
We can also choose to make this a fluent, a diachronic relationship and add a temporal parameter, i.e., semanticShift(l_1, l_2, s, t). However in this case, as in the case of
relationships such as parentOf, we believe that both modelling choices are natural, and we have decided therefore
not to make it a fluent. We also introduce a new object
property between senses senseShiftsTo which enables us
12Conclusions and Further Work

Note that we have been modelling years as intervals through-

out.

21

study of the different kinds of temporal information that arecommon found in dictionaries and legacy lexical resourcesin order to test the robustness and expressivity of our model.

6.

McCrae, J., Spohr, D., and Cimiano, P. (2011). Linking lexical resources and ontologies on the semantic
web with lemon. In Extended Semantic Web Conference,
pages 245–259. Springer.
McCrae, J. P., Bosque-Gil, J., Gracia, J., Buitelaar, P., andCimiano, P. (2017). The OntoLex-Lemon Model: Development and Applications. pages 587–597, September.
Moran, S. and Bruemmer, M. (2013). Lemon-aid: using
lemon to aid quantitative historical linguistic analysis.
In Proceedings of the 2nd Workshop on Linked Data in
Linguistics (LDL-2013): Representing and linking lexicons, terminologies and other language data, pages 28– 33, Pisa, Italy, September. Association for Computational Linguistics.
OUP. (2008). Oxford English Dictionary, Third Edition.
Oxford: Oxford University Press.
Passarotti, M. C., Cecchini, F. M., Franzini, G., Litta, E.,
Mambrini, F., and Ruffolo, P. (2019). The lila knowledge base of linguistic resources and nlp tools for latin.
In 2nd Conference on Language, Data and Knowledge(LDK 2019), pages 6–11. CEUR-WS. org.
Sider, T. (1997). Four-dimensionalism. The Philosophical
Review, 106(2):197–231.
Welty, C., Fikes, R., and Makarios, S. (2006). A reusable
ontology for fluents in owl. In FOIS, volume 150, pages
226–236.

Bibliographical References

Allen, J. F. (1983). Maintaining knowledge about temporal intervals. Communications of the ACM, 26(11):832–
843.
Batsakis, S., Petrakis, E. G., Tachmazidis, I., and Antoniou, G. (2017). Temporal representation and reasoningin owl 2. Semantic Web, 8(6):981–1000.
Bosque-Gil, J., Gracia, J., and Montiel-Ponsoda, E. (2017).
Towards a module for lexicography in ontolex. In LDK
Workshops, pages 74–84.
Caplan, B. and Matheson, C. (2006). Defending musical perdurantism. The British Journal of Aesthetics,
46(1):59–69.
De Melo, G. (2014). Etymological wordnet: Tracing thehistory of words. Citeseer.
Durkin, P. (2009). The Oxford guide to etymology. Oxford
University Press.
Effingham, N. (2012). Endurantism and perdurantism.
The continuum companion to metaphysics, pages 170–
197.
Hansson Wahlberg, T. (2014). Institutional objects, reductionism and theories of persistence. dialectica,
68(4):525–562.
Khan, F. and Bowers, J. (2020). Towards a lexical standard
for the representation of etymological data. In Convegno
annuale dell’Associazione per l’Informatica Umanisticae la Cultura Digitale.
Khan, F., Boschetti, F., and Frontini, F. (2014). Usinglemon to Model Lexical Semantic Shift in Diachronic
Lexical Resources. Proceedings of the Workshop on
Linked Data in Linguistics 2014 (LDL-2014).
Khan, F., Bellandi, A., and Monachini, M. (2016). Tools
and instruments for building and querying diachronic
computational lexica. In Proceedings of the Workshop
on Language Technology Resources and Tools for Digital Humanities (LT4DH), pages 164–171, Osaka, Japan,
December. The COLING 2016 Organizing Committee.
Khan, F., Díaz-Vera, J., and Monachini, M. (2018). Representing meaning change in computational lexical resources: The case of shame and embarrassment terms inold english. Formal Representation and the Digital Humanities, page 59.
Khan, A. F. (2018). Towards the representation of etymological data on the semantic web. Information,
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B. S., Tiberius, C., and Wissik, T. (2018). European lexicographic infrastructure (elexis). In Proceedings of the
XVIII EURALEX International Congress on Lexicography in Global Contexts, pages 881–892.
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22

Proceedings of the 7th Workshop on Linked Data in Linguistics (LDL-2020), pages 23–27
Language Resources and Evaluation Conference (LREC 2020), Marseille, 11–16 May 2020c European Language Resources Association (ELRA), licensed under CC-BY-NC

From Linguistic Descriptions to Language Profiles
Shafqat Mumtaz Virk1 , Harald Hammarström2 , Lars Borin1 , Markus Forsberg1 , Søren Wichmann3
Språkbanken Text, Department of Swedish, University of Gothenburg
2
Department of Linguistics and Philology, University of Uppsala3
Leiden University Centre for Linguistics, Leiden University
3
Laboratory for Quantitative Linguistics, Kazan Federal University
3
Beijing Advanced Innovation Center for Language Resources, Beijing Language University
1{shafqat.virk, lars.borin, markus.forsberg}@svenska.gu.se2{harald.hammarstrom}@lingfil.uu.se3{wichmannsoeren}@gmail.com
1

Abstract
Language catalogues and typological databases are two important types of resources containing different types of knowledge about
the world’s natural languages. The former provide metadata such as number of speakers, location (in prose descriptions and/or GPS
coordinates), language code, literacy, etc., while the latter contain information about a set of structural and functional attributes oflanguages. Given that both types of resources are developed and later maintained manually, there are practical limits as to the numberof languages and the number of features that can be surveyed. We introduce the concept of a language profile, which is intended to
be a structured representation of various types of knowledge about a natural language extracted semi-automatically from descriptive
documents and stored at a central location. It has three major parts: (1) an introductory; (2) an attributive; and (3) a reference part, eachcontaining different types of knowledge about a given natural language. As a case study, we develop and present a language profile ofan example language. At this stage, a language profile is an independent entity, but in the future it is envisioned to become part of a
network of language profiles connected to each other via various types of relations. Such a representation is expected to be suitable bothfor humans and machines to read and process for further deeper linguistic analyses and/or comparisons.
Keywords: Typological information, linguistic descriptions, language networks

1.

Introduction

acteristics (also known as linguistic features) of individual languages. For example, the World Atlas of Language Structures (WALS; Haspelmath et al. 2005), contains information on some 200 features spanning 2500
languages (but is sparsely filled in). A very extensive
list of linguistic databases can be found at http://
languagegoldmine.com/ (accessed 2020-04-05).
These inventories and databases are highly useful resources
but have clear limits on the number of features and/or languages they contain. As such they do not represent all theinformation available about the same language in descriptive publications. This situation is inevitable as (1) a fixed
list of linguistic features is designed for a database, but languages differ from each other in a myriad of ways whichcannot be known a priori; and (2) databases are curated
manually by reading the descriptive documents, which isa time-consuming activity.
For these reasons we aim to go beyond the manual curation of linguistic databases in order to capture the valuable knowledge about many other languages and features
remaining within descriptive publications. Thus, our aim is
to extract all the information about a language describedin a publication, and represent it in a structured manner.
These structured representations can be successively normalized and thus form the basis for large-scale comparison of languages. If successful, it will widen the scope ofinvestigations and comparisons across languages considerably. Toward this end, advancements in natural language
processing and information extraction may be exploited.
A related concern is that various types of knowledge about
languages are maintained separately. Consequently, one has

Approximately 7,000 distinct languages constitute ourrecord of linguistic diversity (Hammarström, 2015). Languages are equal witnesses – where e.g., English is but one– to the variation and constraints of the unique communication system of our species (Evans and Levinson, 2009).
They harbour information on what happens to language
given tens of thousands of millennia of diversification, under all imaginable circumstances of human interaction. As
such they may be used to investigate theories that may otherwise not be testable with anything less than a laboratory
the size of human history.
Two web-publications maintain catalogues of the languagesof the world: Ethnologue (Eberhard et al., 2019) and Glottolog (Hammarström et al., 2019). Ethnologue provides
metadata such as number of speakers, location (in prosewords), literacy etc. Glottolog provides classification, location (in GPS coordinates), and bibliographical references.
For in-depth information about a lesser-known language,
specialists typically consult any available descriptive grammar. For example, for the language Ulwa (ISO 639-3 language code: yla) of Papua New Guinea, there exists
Barlow, Russell. (2018) A grammar of Ulwa.
University of Hawai’i at Mānoa doctoral dissertation. xiv+546 pp.
Around 4,000 languages have at least one published grammatical description but the breadth, depth, and quality ofthese vary (Hammarström et al., 2018).
For analysis of the languages themselves, there are a number of databases which keep a record of various char-23

to explore different resources to access knowledge about
the same language. For example, some general and referential type of data (i.e. about language names, the number
and names of dialects, the areas where they are spoken,
the number of speakers, etc.) are often maintained in theform of digital inventories, the attributive type of data (i.e.
various phonological, morphological, and grammatical features) are maintained as typological databases, and manyother details are found in descriptive documents (grammars, dictionaries, etc.) and, since recently, increasingly in
web-pages, blogs etc.
Further, several of the important resources on natural languages are not open-access. For example, Ethnologue has
most of its information behind a paywall.1 Since only a particular creative arrangement of words – but not facts in general – can be copyrighted, the prospects for free and open
structured representations are much better, even when extracted from copyrighted source materials.
In this paper, we present the concept of a language profile inorder to address the above-mentioned limitations and concerns. A language profile can be envisaged as a digital representation of a natural language containing various typesof information about the language stored at a central location in a structured format and publicly available for further
use. It aims to be a dynamic representation in the sense that
it is not tied to a predefined set of features (like typological
databases), but targets any traceable features. Included arealso introductory and referential information about a target
language extracted from the descriptions and other available resources. Various types of information about a language are grouped into various sections, and the resulting
structure is called a language profile. In the present paper,
we describe the concept of a language profile only. In future work, we plan to describe how language profiles can belinked in a full network (a LangNet) using different kinds ofcomparisons/relations (e.g., genetic, geographical, typological similarity). Conceptually, such a network of languages
is similar to other networks in the area of NLP such as
WordNet, VerbNet, FrameNet, etc., except that it is at thelevel of languages. We believe that such a rich representation model, and the network of languages will be a useful
resource for linguistic studies.
The remainder of the paper is organized as follows: Section
2 describes in detail the structure and components of a language profile, while details on semi-automatic developmentof a language profile from linguistic descriptions are given
in Section 3.

2.

and which manually, and will provide suggestions, wherever possible, for automatizing the corresponding parts.
1. Metadata Part: The metadata part contains basic
metadata such as official language name, number of
speakers, areas where spoken, etc., and referential(e.g., ISO code and/or glottocode, language family,etc.) information. Table 1 shows this part of the ‘Ulwa’language profile.
In this case, most of the fields and their values inthis part of the profile are available in the language
catalogue Ethnologue (Eberhard et al., 2019) in theYaul entry (https://www.ethnologue.com/
language/yla). As such it resembles information
already available in language inventory databases, but
improves on these by being more dynamic, linkable
and aggregateable. The list of possible fields of metadata is not bounded, and can be extended indefinitely.
Each field in the profile and information within it will
have a structured representation. For example, the location in the above given profile is not a simple string,
but rather a geographical location with a name and coordinates. This can be linked to existing inventories
of geographical locations such as GeoNames (http://www.geonames.org). The same applies to thedialect names, families and branches in the classification field, official and alternative language names,
etc. Appropriate data structures will be proposed forvarious fields, with proper IDs to be used for various types of inter- and intra-profile connections. Further, each piece of information will have a recorded
source which may be weighted according to usageneeds whenever there are many different sources forthe same field.
2. Attributive Part: This is the major part of a language profile and is intended to contain the typological and other structural information of a target language. Again, other databases exist with a similar
type of information (e.g., WALS – see above). The
key difference is as follows. The attributive part of alanguage profile does not contain answers to a predefined set of typological and other questions. Rather,it contains all attributive (i.e. phonological, morphological, and grammatical) information which can beextracted (semi)automatically from the available descriptive data about a given language. As an example,
consider the attributive part of the ‘Ulwa’ language
profile given in Table 2. The information in this partwas automatically extracted from a language description (Barlow, 2018). (A description of the automatic
extraction of the typological information is given in
Section 3.)

Language Profiles

As mentioned in the introduction section, a language profile
is necessarily a structured digital representation of a natural language. In this section, we will present the structure
and various proposed components of a language profile. In
doing so, we will use a natural language called ‘Ulwa’, andbuild a minimal part of its profile. At this stage, this language profile will be built semi-automatically, but a longterm objective is to automatize the process as much as possible. We will indicate which parts are built automaticallyIn this example, there is no categorization of the features. In the future, we intend to divide the attributive
part into various subparts e.g. phonological, morphological, grammatical attributive information, and soon. The feature ID field is left blank intentionally atthis stage, and a detailed set of feature IDs is to beworked out at a later stage.

1

Except in third-world countries, where it continues to be
freely available.

24Field-ID
fet:p1:meta-namefet:p1:classification
fet:p1:speakers
fet:p1:dialects
fet:p1:location

Field-Name
Official Name(s)Classification
Speakers
Dialect(s)Location(s)Number::Name::Value Source
1::-::Ulwa(Barlow, 2018)
2::
1::-::Ulmapo(Barlow, 2018)
1::Native::700(Barlow, 2018)
2::Second
Language::
1::-::Manu dialect(Barlow, 2018)
2::-::Maruat-Dimiri(Barlow, 2018)
Yaul
1::-::Manu(Barlow, 2018)
2::-::Maruat(Barlow, 2018)
3::-::Dimiri(Barlow, 2018)
4::-::Yaul(Barlow, 2018)

Table 1: The metadata part of the Ulwa language profile

FeatureID
—
—
—
—
—
—
—
—
—
—
—
—
—
—
—
—
—
—
—
—
—
—
—
—
—
—
—
—
—

FeatureSubject and NP order
Object and NP order
Constituent Order
PostpositionalPhrase–Oblique-markedNP Order
ObliguePhrase–SubjectOFClause Order
ObliguePhrase–Verb Order
Negator–Verb Order
AdPosition–NP Order
Possessor–Possessum Order
Adjective–Noun Order
Demonstrative–Noun Order
Numeral–Noun Order
RelativeClause–HeadNoun Order
PossessivePronoun–Noun Order
ObliqueMarker–Noun Order
TraniativeVerb–ObjectMarker Order
NominalizedVerb–SubjectMarker Order
Verb–DirectObject Order
TransitiveVerb–ObjectMarker Order
Oblique–Verb Order
Oblique- Subject Order
Adverb–Subject Order
Adverb–Object Order
Adverb–Oblique-markedNP Order
NasalSegments–VoicelessStops OrderLabialNasal–PalatoAlveolar Order
HomorganicNasals–VoicelessStops Order
Liquids–LabialStops Order
Liquids–VelarStops Order

Value
NP–SubjectMarker
NP–ObjectMarker
SOV
Both
SubjectOFClause-ObliguePhrase
ObliguePhrase–Verb
Negator–Verb
NP–AdPosition
Possessor–Possessum
Noun–Adjective
Noun–Demonstrative
Noun–Numeral
RelativeClause–HeadNoun
PossessivePronoun–Noun
Noun–ObliqueMarker
TransativeVerb–ObjectMarker
SubjectMarker–NominalizedVerb
DirectObject–Verb
ObjectMarker–TransitiveVerb
Oblique–Verb
Subject–Oblique
Subject–Adverb
Adverb–Object
Adverb–Oblique-markedNPs
NasalSegments–VoicelessStops
LabialNasal–PalatoAlveolar
HomorganicNasals–VoicelessStops
LabialStops–Liquids
VelarStops–Liquids

Table 2: The attributive part of the Ulwa language profile

25

Source(Barlow, 2018)(Barlow, 2018)(Barlow, 2018)(Barlow, 2018)(Barlow, 2018)(Barlow, 2018)(Barlow, 2018)(Barlow, 2018)(Barlow, 2018)(Barlow, 2018)(Barlow, 2018)(Barlow, 2018)(Barlow, 2018)(Barlow, 2018)(Barlow, 2018)(Barlow, 2018)(Barlow, 2018)(Barlow, 2018)(Barlow, 2018)(Barlow, 2018)(Barlow, 2018)(Barlow, 2018)(Barlow, 2018)(Barlow, 2018)(Barlow, 2018)(Barlow, 2018)(Barlow, 2018)(Barlow, 2018)(Barlow, 2018)

3. References Part: The reference part contains a list ofavailable resources about the language at hand. A BibTeX type of entry will be maintained for each descriptive document and other type of resource (e.g. word
list, dictionary, etc.). One such entry for the ‘Ulwa’language is as follows:}

ley FrameNet (Baker et al., 1998) and other, domainspecific framenets (e.g. a framenet to cover medical terminology (Borin et al., 2007), Kicktionary,2 a soccer language
framenet). Let us take an example to better understand what
LingFN is, and how its frame-labeled data is used to build
the frame-semantic parser which in turn is used for automatic extraction of typological features. Consider the following sentence which is taken from a descriptive grammarof the Ulwa language.

@phdthesis{g:Barlow:Ulwa,
author = {Barlow, Russell},title = {A grammar of Ulwa},
school = {University of Hawai’i
In Ulwa, adjectives in NPs sometimes precede
at Mānoa},
their head nouns.
pages = {xiv+546},
year = {2018},
The sentence contains information about the relative poglottolog_ref_id = {554079},
sition (sequencing) of two syntactic categories i.e. ‘adjechhtype = {grammar},tives’ and ‘head nouns’. Their position wrt one another isinlg = {English [eng]},
not always the mentioned one but could be the other way
lgcode = {Manu Ulwa = Yaul [yla]}, around, as conveyed by the adverb ’sometimes’. This is usemacro_area = {Papua}
ful information that we are interested in extracting automat-

ically. One of the possible approaches is to develop a framesemantic based information extraction system. For that purpose, the first step is to design (or use from the Berkeley
FrameNet) special structures to represent this type of phenomenon (i.e. sequencing). In frame-semantics such structures are called semantic frames, and in general, a semantic
frame is a structured representation of a an entity, an object,
and a scenario. In our case, a semantic frame represents
a linguistic entity (e.g. nouns, verbs, etc.) or phenomenon(e.g. affixation, agreement, sequence, etc). Let us say forthe sequencing phenomena, we have designed a semantic
frame with the structure shown in Table 3. (For more details
on development of the SEQUENCE and other linguistic domain semantic frames with annotated example sentences,
we refer the reader to Malm et al. (2018)).

Every item of information in each section of the language
profile has a source linked to an entry from the reference
section. The maintenance of references within the profile
ensures that the crucial source links can be kept in sync.

3.

Building a Language Profile

Building a language profile is a complex process. It requires gathering information about a language from allavailable sources, i.e., manuals, digital inventories, linguistic descriptions, etc. This is a long-term process, and will
require gradual efforts to incrementally develop a large setof rich language profiles.
At this stage, we have relied on manual collection of information for the introductory as well as the reference part,
although parts of it can be automatized (information about
language name and number of speakers can be extracted automatically using the frame based methodology explained
below, which was used to build the attributive part automatically).
The automatic extraction of typological information fromdescriptive grammars is a novel task, and there exists only
a few studies and systems reported previously (Virk et al.,2017; Virk et al., 2019). In Virk et al. (2019), a framesemantic based approach is proposed for developing aparser to automatically extract typological linguistic information from plain-text grammatical descriptions of natural
languages. As a case study, the authors have shown how theparser can be used to extract value of an example typological feature. However, the system has not been used for any
actual typological work. We continue that work and use theparser to extract typological feature values (shown in Table
2) of a language profile. A brief description of the parser
and how it has been used for our purposes follows.
The parser relies on a lexico-semantic resource, LingFN(Malm et al., 2018), and its frame-labeled data for training machine learning models to build a parser. The development of LingFN itself is based on the theory offrame-semantics (Fillmore, 1976; Fillmore, 1977; Fillmore,1982), and is motivated by the development of Berke-

SEQUENCE
Entity 1
Entity 2
Entities 3
Order
Frequency
Language Variety
Table 3: Sequence Semantic Frame
Entity 1, Entity 2, Entities, Order, Frequency, and Language Variety are referred to as frame-elements, whichconstitute various semantic parts of the sequencing phenomena. With such structures (i.e. semantic frames) athand, the next step is to annotate linguistic descriptions
with developed semantic frames. The annotation of theabove given sentence is as follows:
In [Ulwa]_Language Variety,[adjectives]_Entity_1 in NPs[sometimes]_Frequency[[precede]_LU]_Order
their [head nouns]_Entity_2.
226

http://www.kicktionary.de/6.

String segments labeled as one of the frame-elements are
enclosed within a pair of brackets while the frame-element
label (bold) follows an underscore sign. Note that in case ofabove given sentence, the word ’precede’ is both a lexical
unit (a word triggering a particular frame) and also a frameelement.
Now imagine, if we have enough sentences annotated with
the SEQUENCE (and other frames from LingFN), one
could train machine learning models for automatic labeling of these frames on un-annotated data. This is exactly
what is proposed by the authors in (Virk et al., 2019), andthey have a developed a parser for this purpose. What theparser does is to take un-annotated sentences containing typological linguistic information and annotate them with linguistic domain frames and their associated frame-elements.
As suggested by the authors in the same paper, the annotations can be converted to typological information in any
required format using a rule based module. This is exactly
what we have done to extract feature values shown in Table
2 for the Ulwa language. Note, only the SEQUENCE frame
was used to extract the whole information present in Table
2. In the future we plan to extend this work to other typological features and hence enhance the attributive part of alanguage profile.

4.

Conclusions and Future Work

We have presented the idea of a language profile, which isenvisaged as a digital structured representation of a natural
language. It has two major objectives. The first objective
is to overcome a major limitation of existing typological
databases which contain information about a pre-defined
set of linguistic features. We propose work towards automatically extracting information about all the features described in a descriptive document. The second objective is
to collect various types of information available about a language stored in a structured way and at a common place together with information about the sources. The idea is at anembryonic stage and is to be further matured and extended
in the future.

5.

References

Baker, C. F., Fillmore, C. J., and Lowe, J. B. (1998).
The Berkeley FrameNet project. In Proceedings of
ACL/COLING 1998, pages 86–90, Montreal. ACL.
Barlow, R. (2018). A grammar of Ulwa. Ph.D. thesis, University of Hawai’i at Mānoa.
Borin, L., Toporowska Gronostaj, M., and Kokkinakis, D.
(2007). Medical frames as target and tool. In FRAME
2007: Building Frame Semantics Resources for Scandinavian and Baltic Languages. (Nodalida 2007 Workshop
Proceedings), pages 11–18, Tartu. NEALT.
Eberhard, D. M., Simons, G. F., and Fennig, C. D. (2019).
Ethnologue: Languages of the World. Dallas: SIL International, 22 edition.
Evans, N. and Levinson, S. (2009). The myth of language universals: Language diversity and its importancefor cognitive science. Behavioral and Brain Sciences,32(5):429–492.
Fillmore, C. J. (1976). Frame semantics and the nature oflanguage. Annals of the New York Academy of Sciences,280(1):20–32.
Fillmore, C. J. (1977). Scenes-and-frames semantics. In
Antonio Zampolli, editor, Linguistic Structures Processing, pages 55–81. North Holland, Amsterdam.
Fillmore, C. J. (1982). Frame semantics. In Linguistic Society of Korea, editor, Linguistics in the Morning Calm,pages 111–137. Hanshin Publishing Co., Seoul.
Hammarström, H., Castermans, T., Forkel, R., Verbeek, K.,
Westenberg, M. A., and Speckmann, B. (2018). Simultaneous visualization of language endangerment and language description. Language Documentation & Conservation, 12:359–392.
Hammarström, H., Forkel, R., and Haspelmath, M. (2019).
Glottolog 4.0. Jena: Max Planck Institute for the
Science of Human History. Available at http://
glottolog.org. Accessed on 2019-09-12.
Hammarström, H. (2015). Ethnologue 16/17/18th editions: A comprehensive review. Language, 91(3):723–
737. Plus 188pp online appendix.
Martin Haspelmath, et al., editors. (2005). World Atlas ofLanguage Structures. Oxford: Oxford University Press.
Malm, P., Virk, S. M., Borin, L., and Saxena, A. (2018).
Lingfn: Towards a domain-specific linguistic framenet.
In Tiago Timponi Torrent, et al., editors, Proceedings of the Eleventh International Conference on Language Resources and Evaluation (LREC 2018), Paris,
France, may. European Language Resources Association(ELRA).
Virk, S., Borin, L., Saxena, A., and Hammarström, H.
(2017). Automatic extraction of typological linguisticfeatures from descriptive grammars. In Proceedings of
TSD 2017, pages 111–119, Cham. Springer.
Virk, S. M., Muhammad, A. S., Borin, L., Aslam, M. I.,
Iqbal, S., and Khurram, N. (2019). Exploiting framesemantics and frame-semantic parsing for automatic
extraction of typological information from descriptive
grammars of natural languages. In RANLP-Proceedings,
sep.

Acknowledgements

The work presented here was funded by (1) the Dictionary/Grammar Reading Machine: Computational Tools
for Accessing the World’s Linguistic Heritage (DReaM)
Project awarded 2018–2020 by the Joint Programming Initiative in Cultural Heritage and Global Change, Digital Heritage and Riksantikvarieämbetet, Sweden; (2) the Swedish
Research Council as part of the project South Asia as a linguistic area? Exploring big-data methods in areal and genetic linguistics (2015–2019, contract no. 421-2014-969);(3) From Dust to Dawn: Multilingual Grammar Extraction
from Grammars project funded by Stiftelsen Marcus och
Amalia Wallenbergs Minnesfond 2007.0105, Uppsala University; and (4) the University of Gothenburg, its Faculty of
Humanities and its Department of Swedish, through their
truly long-term support of the Språkbanken Text research
infrastructure.

27

Proceedings of the 7th Workshop on Linked Data in Linguistics (LDL-2020), pages 28–35
Language Resources and Evaluation Conference (LREC 2020), Marseille, 11–16 May 2020c European Language Resources Association (ELRA), licensed under CC-BY-NC

Terme-à-LLOD: Simplifying the Conversion and Hosting of Terminological
Resources as Linked Data
Maria Pia Di Buono1 , Philipp Cimiano2 , Mohammad Fazleh Elahi2 , Frank Grimm2
1

UNIOR NLP Research Group, University of Naples L’Orientale
2
Semantic Computing Group, Bielefeld University
mpdibuono@unior.it{cimiano, melahi, grimm}@cit-ec.uni-bielefeld.de
Abstract

In recent years, there has been increasing interest in publishing lexicographic and terminological resources as linked data. The benefit
of using linked data technologies to publish terminologies is that terminologies can be linked to each other, thus creating a cloud of
linked terminologies that cross domains, languages and that support advanced applications that do not work with single terminologies
but can exploit multiple terminologies seamlessly. We present Terme-à-LLOD (TAL), a new paradigm for transforming and publishingterminologies as linked data which relies on a virtualization approach. The approach rests on a preconfigured virtual image of a serverthat can be downloaded and installed. We describe our approach to simplifying the transformation and hosting of terminologicalresources in the remainder of this paper. We provide a proof-of-concept for this paradigm showing how to apply it to the conversionof the well-known IATE terminology as well as to various smaller terminologies. Further, we discuss how the implementation of
our paradigm can be integrated into existing NLP service infrastructures that rely on virtualization technology. While we apply thisparadigm to the transformation and hosting of terminologies as linked data, the paradigm can be applied to any other resource format as
well.
Keywords: Linguistic Linked Open Data, Terminological Resources, NLP services

1.

Introduction

ing linked data and Semantic Web technologies. For example, as a result of the EC-funded LIDER project1 and as partof the work of the W3C community group on Best Practices
for Multilingual Linked Open Data (BPMLOD)2 , guidelines have been released describing how to publish terminologies in TBX format as linked data using the ontolexlemon model (McCrae et al., 2011; McCrae et al., 2015).
More recently, the Linked Heritage Project3 has released
recommendations for how to manage terminologies in theframework of the Semantic Web.
Yet, a fundamental problem remains, that is that implementing all these guidelines and recommendations is challenging as one needs a detailed understanding of the corresponding vocabularies in addition to technical understanding of data models (e.g., RDF) as well as how to host linked
data at a server level. We present a new approach that wecall Terme-à-LLOD (TAL), aiming to fill this gap and simplifying the task of converting a terminological resource in
TBX format into a linked data resource and ease the task ofhosting the linked data resource in such a way that i) URIs
resolve, ii) the resource can be browsed, and iii) a SPARQL
endpoint is offered. This new paradigm for transforming and publishing standardized terminological resources
as linked data relies on a virtualization approach. The approach rests on a pre-configured virtual image of a serverthat can be downloaded and installed. In our approach werely on Docker4 , but any other virtualization environment
can be used instead.
The remainder of the paper is organized as follows: Section

Terminological resources, mainly termbases, represent
a core source of data for translation and localization(Stanković et al., 2014). Further, they have important applications in text mining as they provide concepts with whichelements of text can be tagged for semantic normalization
as well as semantic indexing (Witschel, 2005). Therefore,
many intermediate representations of terminological information and tools for termbase management have been developed so far with the main goal of improving the portability and interoperability of those resources. Among the representations of terminological information, the TermBase
eXchange (TBX) format has become a standard for terminology information exchange. Such an exchange plays animportant role in ensuring consistency and contributes toterminology production and quality through interactive validation processes (joh, ).
In recent years, there has been interest in publishing terminological resources as linked data in order to improve interoperability and reuse and a number of approaches proposing to use linked data principles to publish terminologies
have been proposed (Cimiano et al., 2015; RodriguezDoncel et al., 2015; Montiel-Ponsoda et al., 2015).
The general benefits of publishing language resources aslinked data have been described by Chiarcos et al. (2013).
In short, the benefit of using linked data technologies topublish terminologies is that terminologies can be linked toeach other, in order to create a cloud of linked terminologies
that cross domains, languages and that support advanced
applications that do not work with single terminologies but
can exploit multiple terminologies seamlessly (see the workof Montiel et al. (2015) on integrating two terminologies,
TERMACT and Terminesp, using Linked Data). Alongthese lines, a number of projects have published specificguidelines on how to publish terminological resources us-1

http://www.lider-project.eu/
lider-project.eu/index.html
2
https://www.w3.org/2015/09/
bpmlod-reports/multilingual-terminologies/3
http://linkedheritage.eu/
4
https://www.docker.com

28

2. describes our new approach and its virtualization process
to simplify the transformation and hosting of terminological resources. Following this, Section 3. presents two casestudies comprising the transformation of the Inter-Agency
Terminology Exchange (IATE)5 terminology as well as asample of termbases provided by the Centrum Voor Terminologie (CvT)6 at Ghent University into Linked Data.
Finally, Section 4. shows how our approach can be integrated into existing NLP service infrastructures that rely on
virtualization technology such as European Language Grid
(ELG)7 and Teanga8 . Section 5. discusses related work andhow our approach can be integrated into other NLP service
frameworks. The paper ends with the conclusion and future
work.

2.

<termEntry id="IATE-84"><descripGrp><descrip type="subjectField">1011</descrip></descripGrp><langSet xml:lang="en"><tig><term>competence of the Member States</term><termNote type="termType">fullForm</termNote><descrip type="reliabilityCode">3</descrip></tig></langSet><langSet xml:lang="da"><tig><term>medlemsstatskompetence</term><termNote type="termType">fullForm</termNote><descrip type="reliabilityCode">3</descrip></tig></langSet><langSet xml:lang="nl">.....
</langSet><langSet xml:lang="es">.....
</langSet>.....
</termEntry>

Terme-à-LLOD Approach

Terme-à-LLOD is a new virtualization paradigm for easing the process of transforming terminological resources
into RDF and hosting them as linked data. The virtualization paradigm relies on three main components (Figure2): a converter (A), a Virtuoso Server9 (B) (Erling and
Mikhailov, 2010; Erling, 2012), and a container (C).
The converter element managing the automatic format
transformation is based on the TBX2RDF service10 (Cimiano et al., 2015) developed by the LIDER project.
TBX2RDF maps TBX inputs, including TBX public dialects, i.e., TBX-Core, TBX-Min and TBX-Basic, into
RDF format, reusing a set of classes and properties fromexisting linked open data vocabularies (e.g., OntoLexLemon11 ). An example of converting TBX to RDF is canbe seen in Figure 2.
The converter produces an RDF output which serves as input to a Virtuoso server, the second component of the TAL
virtualization technology. Once the RDF output has been
uploaded, the pre-installed server, which hosts the service,
exposes the converted data through an endpoint which allows to access them. The server also provides a SPARQL
endpoint to other services.
The third element of the virtualization technology is aDocker container that can be easily installed on any Docker
environment. The Docker container allows to bundle components, libraries and configuration files of the TAL service
and to run the service on different computing environments.
Once the container is installed and instantiated, theterminological resource can be pushed via HTTP/Advanced Message Queuing Protocol (AMQP) request to the
TBX2RDF converter. Subsequently, the TAL service invokes the transformation to Linked Data using the converterand hosts the resulting RDF as linked data together with a
SPARQL endpoint.
The benefit of such a virtualization approach is that theowner of a terminology can easily publish the terminology<http://webtentacle1.techfak.uni-bielefeld.de/tbx2rdf_iate/competence+of+the+Member+States-en>a ontolex:LexicalEntry ;dct:language <http://www.lexvo.org/id/iso639-3/eng> ;tbx:reliabilityCode 3 ;<http://www.lexinfo.net/ontology/2.0/lexinfo#termType><http://www.lexinfo.net/ontology/2.0/lexinfo#fullForm> ;<http://www.w3.org/ns/lemon/lime#language>"en" ;
ontolex:canonicalForm <http://webtentacle1.techfak.uni-bielefeld.de/tbx2rdf_iate/data/iate/competence+of+the+Member+States-en#CanonicalForm> ;ontolex:sense <http://webtentacle1.techfak.uni-bielefeld.de/tbx2rdf_iate/data/iate/competence+of+the+Member+States-en#Sense> .
<http://webtentacle1.techfak.uni-bielefeld.de/tbx2rdf_iate/
data/iate/dal%C4%ABbvalstu+kompetence-lv#Sense>
a ontolex:LexicalSense ;ontolex:isLexicalizedSenseOf :IATE-84 .
<http://webtentacle1.techfak.uni-bielefeld.de/tbx2rdf_iate/
data/iate/competence+of+the+Member+States-en#CanonicalForm>
ontolex:writtenRep "competence of the Member States"@en .
<http://webtentacle1.techfak.uni-bielefeld.de/tbx2rdf_iate/
data/iate/medlemsstatskompetence-da#CanonicalForm>
ontolex:writtenRep "medlemsstatskompetence"@da .
.......

Figure 1: TBX (top) to RDF (bottom) conversion

as linked data without the need to understand the underlying vocabularies in detail nor of the RDF data model orabout how to set up a linked data server. Yet, the dataremains under full control and can be published under anamespace to represent ownership and provenance.

2.1.

Virtualization Process

The virtualization technology is contained into a preconfigured virtual image that can be hosted in a corresponding environment consisting of virtual machines communicating with each other over standard protocols. All capabilities of a TAL service are advertised in an OpenAPI descriptor file. This lets consumers discover how to communicate
with the service and what result values to expect.
The TAL service automatically gathers the latest versionof the TBX2RDF service from GitHub and installs it in a
multi-stage container build that makes knowledge of theunderlying Java development stack transparent to the end
user. TAL adds a Node.js application behind a nginx reverse proxy for HTTP communication with the service.
This application is used to orchestrate the different internals of the container and monitor the status or health of thecontainer.
The service is initially provided with term glossaries
as standardized TBX files, as defined by ISO standard

5

https://iate.europa.eu
http://www.cvt.ugent.be7
https://www.european-language-grid.eu/
8
https://teanga.techfak.uni-bielefeld.de/9
https://virtuoso.openlinksw.com/
10
http://tbx2rdf.lider-project.eu/
converter/11
https://www.w3.org/2016/05/ontolex/
6

29

(
C)

(
A)

(
B)

Figure 2: TAL virtualization paradigm
30042:2008, as well as an optional file containing mapping information between TBX and the desired target RDF
namespace. A stack of open source software is then used
to convert and deploy the glossary data in queryable linked
open data formats, namely as a resource description framework (RDF) download and a SPARQL endpoint to query
this data.
The container bundles the TBX2RDF converter, implemented as a Java program that reads in the document andbuilds the DOM tree. The DOM tree is traversed and elements are mapped to appropriate object-oriented datastructures. These datastructures are then serialized as RDF.
The resulting RDF is exposed to a web server for further
downstream tasks that require the full dataset and automatically populates an instance of the Virtuoso open source
database engine12 .
Since terminology databases can be of considerable size,
the container additionally exposes a status application thatallows an end user to monitor the conversion progress andstatus of each service instance. During regular use, theecosystem issues a new instance of the TAL Docker container that is available on GitHub13 . It is either initialized
as an empty instance or provided with the state or database
content of a previously created instance.
The only knowledge required to setup a service instance isminimal and generally regards the specific ecosystem used
to work with the service. Specialized knowledge of TBX,LLOD or NLP technologies is not necessary and abstracted
away in order to make these resources more approachable.
In fact, a Docker container is a lightweight, standalone, executable package (“container image”) of software that canbe seen as a template to bootstrap everything required to run
an application: code, runtime, a lean operating system, sys-

tem libraries and settings. The Docker engine (e.g., pure
Docker, Kubernetes or a platform-as-a-service cloud offering) enables containerized applications to run anywhereconsistently on essentially any infrastructure. A Docker
volume is used to retain the results of costly conversion
processes across updates and reboots. During bootup the
TAL container starts a Node.js application and nginx web
server. The service is immediately discoverable through the
OpenAPI descriptor. The conversion process itself has toonly be run once, e.g., by the party maintaining a particularterminology. Subsequent users can consume from the initialized service instance by either post-processing the generated RDF artifact that is exposed via HTTP or querying
the SPARQL endpoint that hosts the resulting linked data
structures.

3.

Use Cases: IATE and GENTERM

In order to provide a proof-of-concept of this approach
to simplify the process of transforming terminological resources into RDF and hosting the RDF as linked data, weused a sample of data from two sources. The data sourceis the Inter-Agency Terminology Exchange (IATE) repository and the second are a number of termbases hosted by
the Centrum Voor Terminologie (CvT) at Ghent University.

3.1.

IATE

IATE, a central terminology database for all the institutions,
agencies and other bodies of the European Union, provides
a single access point to the existing European terminological resources, besides an infrastructure for the constitution,
shared management and dissemination of these resources(joh, ). With a current total number of 935K entries, 7.1
MM terms and 26 languages14 , this database represents thereference in the terminology field, and is considered to be12

http://vos.openlinksw.com/owiki/wiki/VOS
https://github.com/ag-sc/terme-a-llod andhttps://hub.docker.com/r/agsc/terme-a-llod
13

14

30

https://iate.europa.eu/download-iate

TBX field
TBX resource
TermLangset
TIG/NTIG
TermGrp
TermCompList
TermCompGrp
DescrGrp
TransGrp/Transaction

RDF element
void:Dataset
skos:Concept
ontolex:Lexicon
ontolex:LexicalEntry
ontolex:canonicalForm
ontolex:decomp
decomp:correspondsTo
properties of the lexical entry
tbx:Transaction

Table 1: Conceptual mapping of TBX fields and RDF elements.
TBX input
IATE
Pharmaceutical*
Diseases*
Waste
management*
Solar energy*
Printmaking*Runtime
25.2m
5.2s
3.0s
2.5s

# Terms
5851035
4629
799
396

# Triples
52603182
71347
12650
6109

# Lang
25
2
2
2

2.9s
2.5s

205
223

3758
3426

2
2

Table 2: Information about IATE and GENTERM conversion process (Entries marked with * are courtesy of GENTERM).
TBX Ontology. The TBX fields we consider during theconversion process and the mapping elements selected from
aforementioned vocabularies are shown in Table 1. The
TBX Resource field is not explicitly represented, as thewhole dataset represents the TBX resource. A TBX resource is thus represented as a void:Dataset to which provenance and licensing information can be attached. Furthermore, a langset is not represented as such in the data. Instead, one ontolex:Lexicon is created for each language
for which a LangSet is defined. The collection of all theterms for a given language will belong to the corresponding language-specific ontolex:Lexicon. The DescrGrp fieldcontains descriptions of the term or context that are mapped
to appropriate properties of the lexical entry or the context.
A general overview of the conversion process is available
in Table 2. For each termbasis used in the conversion process, we present the runtime needed, the number of terms
stored in the termbasis, the number of triples resulting in
the output files, and the number of languages we converted.
Figure 3 shows an example of TAL final output, namely
the exposure of an RDF terminological resource which canbe browsed to access more specific information about eachterm.

the largest multilingual terminology database in the world.
Data, provided in TBX format and made available withouta copyright protection, can be freely downloaded and reproduced, for personal use or for further non-commercial
or commercial dissemination15 .

3.2.

GENTERM

The second sample of data has been extracted from thetermbases developed by the Centrum Voor Terminologie(CvT) - GENTERM16 . The center, active within the Department of Translation, Interpreting and Communicationof Ghent University, co-ordinates the Department’s activities on terminology and terminography and makes available
a small set of termbases, which are the result of several
students’ projects. GENTERM termbases belong to different domains (e.g., pharmaceutica, waste management, solar energy, diseases, printmaking). We provide a proof-ofconcept of the workins conversion with all these six termbases.

3.3.

Transformation to linked data

We converted the IATE and CvT TBX terminologies using
our Terme-à-LLOD service and expose test instances on acentral demonstration server17 that can be used in combination with other workflows.
As already mentioned, the conversion process is mainly
based on the use of the TBX2RDF converter. Several vocabularies, mainly W3C recommendations, have been usedduring the conversion process, namely OntoLex-lemon,
SKOS, RDF-schema, DCAT, VOID, PROV-O, LIDER

3.4.

Linking IATE and GENTERM

After the conversion process, we established links among
the different termbases tested in the use case by meansof Simple Knowledge Organization System (SKOS)18 concepts. The linking across GENTERM and IATE terminologies has been accomplished matching the corresponding lexical entries in different languages by means of a15

https://iate.europa.eu/legal-notice
http://www.cvt.ugent.be/downloads.htm
17
http://scdemo.techfak.uni-bielefeld.de/termeallod/
16

18

SKOS is a vocabulary for representing knowledge organization systems (KOS), such as thesauri, classification schemes, subject heading and taxonomies in RDF.

31

Figure 3: Example of converted terminological resources exposed in TAL service.
string comparison based on skos:exactMatch. This
match allows linking, for instance, the term nefopam, fromthe Pharmaceutical termbasis in GENTERM, to the corresponding term in IATE, which has an alpha-numeric identifier, i.e., IATE-3545983. Once links among the terminologies have been established, users can explore a term acrossall the converted and exposed termbases.
Table 3 shows the number of links between GENTERM
and IATE. Even though the GENTERM terminology covers
different domains in two languages (English and Dutch),
the termbases available are very small in comparison to
IATE. This explains the low number of links for some of the
proposed domains, e.g., GENTERM Solar energy-IATE in
Table 3.

4.

duce the manual effort required to process data. One of
the main issues in these proposals is the need of following specific standards to develop NLP services which interoperate smoothly on the platform. To address this issue,
Teanga uses linked data and open, semantic technologies
to describe the boundaries between different services interms of application programming interface (API) descriptors. Given an API endpoint, these descriptors can be used
to automatically discover the capabilities of a particular service and specify data types of possible inputs and outputs
to the particular NLP service.
ELG is an initiative to establish the primary platform for
language technologies (LT) in Europe (Rehm et al., Forthcoming). Its main goal is involving several stakeholders
from the language technology sector to create a community
which shares technologies and data sets through the platforms, deploys them through the grid and connects themwith other resources. ELG addresses some of the recommendations identified in the European Parliament resolution of 11 September 2018 on language equality in the digital age, namely creating a European LT platform for sharingof services and enabling and empowering European SMEs
to use LTs.

Integration into language infrastructures

Language infrastructures represent one of the leading areas
for the digital economic growth19 , as well as a key element
to enable an inclusive Digital Single Market20 . Several initiatives and projects aim at providing tools supporting interoperability and sharing of existing language technologies
and data sets. In order to contribute to the development ofcommon language technologies and support these sharing
initiatives, as a proof-of-concept we have developed an approach to integrate our Terme-à-LLOD approach into two
language infrastructures, namely Teanga and ELG. Furthermore, such an integration proves the interoperability of ourapproach which relies on virtualization services.
Teanga is a linked data based platform for natural language
processing (NLP) which enables the use of many NLP services from a single interface (Ziad et al., 2018). Many platforms have been developed to improve the interoperability among different NLP services and, consequently, re-

The grid platform has been built with robust, scalable, reliable, widely used technologies that are constantly developed further. It presents the ability to scale with thegrowing demand and supply of resources through an interactive modern web interface, providing the base technology for a catalogue or directory of functional services,
data sets, tools, technologies, models and LT companies,
research organisations, research projects, service and application types, languages. ELG deals with several typesof content, that is, services, language resources, data sets,
tools and directory content. The TAL service presented inthis paper belongs to the type functional content, comprising of containerized services that can be uploaded and integrated into other systems. To support this integration, the19
https://www.tractica.com/research/
natural-language-processing/
20
https://ec.europa.eu/commission/priorities/digital-single-market_en32Termbases
GENTERM Pharmaceutical-IATE

LangEnglishDutch
EnglishDutch
EnglishDutch
EnglishDutch
English
Dutch

GENTERM Diseases-IATE
GENTERM Waste management-IATE
GENTERM Solar energy-IATE
GENTERM Printmaking-IATE

Number of Links
1380
1084
22
27
114
109
12
20
35
21

Table 3: Results from the linking process.

Figure 4: Integration of TAL service into language infrastructures
ELG platform provides an easy and efficient way for LT
providers to create and upload containers (Rehm, 2019).
The integration of our TAL service into Teanga and ELG(Figure 4) enables the ecosystem to incorporate data fromexternal terminology glossaries as linked data. Such an integration, based on the development of a docker container,
increases the usability of our service, providing a principled way for integrating our service into other applications
consuming the terminological data. The ELG cluster andthe workflow of Teanga application, namely the integration
servers, can query the OpenAPI descriptor in TAL service.
When used as a service endpoint, the service automaticallyconnects to a message queue via the AMQP that acts as aservice bus for inter-service communication and lets othersoftware consume data.
In recent years, containerization replaced many use cases
where the only previous option was full virtualization of asystem. Especially in modern, service-based, architectures
virtualization often implied many of the same hardships
as maintaining a system on bare metal machines, includ-ing operating system maintenance and the ability to scale
such a system still being costly and subject to the underlying virtualization infrastructure. By introducing containers, most of these aspects of software deployments are abstracted into purely operational questions and become practically transparent to the developer. The containerizationof the TAL service ensures portability across systems by
abstracting the underlying hardware following a virtualization approach while at the same time supporting efficient
deployment of the application.

5. Related Work
The TermBase eXchange (TBX) format has become an international standard (ISO 30042:2019)21 for exchange ofterminological information. It allows the representation of
structured concept-oriented terminological data providing
21

For this documentation we refer to the official documentation
available at https://www.gala-global.org/sites/
default/files/uploads/pdfs/tbx_oscar_0.pdf

33

Infrastructure (CMDI) 25 . CMDI describes functionalities
offered by a service, pre and postconditions, and specifications of data that is consumed and produced by service.
The Language Application (LAPPS)26 (Ide et al., 2014)
Grid is a framework that provides access to NLP processing tools and resources and enables pipelining these tools
to create custom NLP applications, as well as access to language resources such as mono- and multilingual corpora
and lexicons that support NLP. The semantic interoperability of language services is achieved by the Web Services
Exchange Vocabulary (Ide et al., 2016), which specifies terminology for a core of linguistic objects and features exchanged by services. Recently, the services are deployedin the cloud using Docker images. While we have integrated our TAL service into ELG and Teanga as a proof-ofconcept, it could also be integrated into the WebLicht enviroment as well as LAPPS Grid following the same principles.

an XML-based framework to manage terminology, knowledge and content by means of several processes, such as
analysis, descriptive representation, dissemination, and interchange (exchange). OntoLex-Lemon has been proposed
early on as a Linked Data format for representing terminological resources (Cimiano et al., 2015). Ontolexlemon has been applied to the conversion of Terminesp
into Linked Data (Bosque-Gil et al., 2015) as well as tothe transformation of a set of freely available terminology
databases from the Catalan Terminological Centre, TERMCAT (Montiel-Ponsoda et al., 2015).
Guidelines for converting TBX data to Linked Data havebeen developed as part of the LIDER project22 . These
guidelines explain how the TBX data model can be mapped
to the Ontolex-lemon model and provides a step-by-step example how TBX data can be transformed into RDF following this mapping.
An alternative set of recommendations and guidelines havebeen developed by the Linked Heritage and Athena projects(Leroi et al., 2011). The document proposes a three-stepmethodology to digitalize terminologies for publication in
the Semantic Web consisting of three three steps: i) conceive your terminology, ii) make your terminology interoperable, iii) link your terminology to a network. As datamodel, the document proposes to use the SKOS model
rather than the Ontolex-lemon model.
The approach proposed in this paper has focused on thetransformation of terminological resources; yet, the principled approach of simplifying the work of transforming
resources into RDF would apply to other data formats aswell. There has been some work on transforming lexicographic resources as well as WordNets into Linked Data
using lemon-Ontolex (McCrae et al., 2012; Eckle-Kohler
et al., 2015; Ehrmann et al., 2014; McCrae et al., 2014).
There has been work on transforming corpora into RDF(Chiarcos and Fäth, 2017). The approach described here
could be applied to those data formats as well.
We have integrated our transformation component into theTeanga (Ziad et al., 2018) and ELG (Rehm et al., Forthcoming) infrastructrues. There are other NLP architecuresinto which TAL container could be integtated.
WebLicht23 is an environment for building, executing, andvisualizing the results of NLP pipelines, which is integrated
into the CLARIN infrastructure (Hinrichs and Krauwer,2014). NLP tools are implemented as web services that
consume and produce the Text Corpus Format (TCF)24 , an
XML format designed for use as an internal data exchange
format for WebLicht processing tools. It ensures semantic interoperability among all WebLicht tools and resources
by defining a common vocabulary for linguistic concepts in
TCF schema. The services and resources are developed as
web services in the CLARIN framework. The services are
exposed using metadata descriptions Component Metadata

6.

Conclusion

We have proposed a virtualization approach to support theconversion and hosting of terminologies as linked data. The
approach can in principle be applied to any language andlexical resource beyond terminologies using the same principles.
We have demonstrated the applicability of our approach
via the conversion into RDF and hosting as linked data ofsix terminologies in total: the well-known IATE termbase
and five smaller termbases hosted by Ghent University. A
public Docker container has been implemented and is freeavailable for everyone wanting to convert and host theirterminologies. We have described the integration of ourapproach into state-of-art language infrastructures, namely
Teanga and ELG.
Within the European project Prêt-à-LLOD27 , which focuseson making linguistic data ready to use by the use of state-ofthe-art technologies, in particular linked data, a further integration of this service is currently planned. Prêt-à-LLOD
aims at creating a new methodology for building data value
chains applicable to a wide-range of sectors and applications and based around language resources and language
technologies that can be integrated by means of semantic
technologies. The Terme-à-LLOD approach proposed here
follows the aims of the EC-funded Prêt-à-LLOD project ofproviding Linked-data based NLP services as data so thatthey are sustainable and can be readily used and deployedby third parties. In future work we will enhance the architecture and implementation of the TAL service towards
supporting linking any other linked data compliant terminology to the one hosted by TAL.

7. Acknowledgements
This work has been funded by project Prêt-à-LLOD which
is funded under European Union’s Horizon 2020 researchand innovation programme under grant agreement No.
825182.

22https://www.w3.org/2015/09/
bpmlod-reports/multilingual-terminologies/
23
https://weblicht.sfs.uni-tuebingen.de/weblichtwiki/index.php/Main_Page24
https://weblicht.sfs.uni-tuebingen.de/weblichtwiki/index.php/The_TCF_Format

25
https://www.clarin.eu/content/
component-metadata26
http://www.lappsgrid.org/
27
https://www.pret-a-llod.eu

34

8.

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Proceedings of the 7th Workshop on Linked Data in Linguistics (LDL-2020), pages 36–44
Language Resources and Evaluation Conference (LREC 2020), Marseille, 11–16 May 2020c European Language Resources Association (ELRA), licensed under CC-BY-NC

Annohub – Annotation Metadata for Linked Data Applications
Frank Abromeit, Christian Fäth, Luis Glaser
Applied Computational Linguistics Lab, Goethe University Frankfurt, Germany{abromeit,faeth}@em.uni-frankfurt.de, lglaser@informatik.uni-frankfurt.de
Abstract

We introduce a new dataset for the Linguistic Linked Open Data (LLOD) cloud that will provide metadata about annotation and language
information harvested from annotated language resources like corpora freely available on the internet. To our knowledge annotation
metadata is not provided by any metadata provider, e.g. linghub, datahub or CLARIN so far. On the other hand, language metadata thatis found on such portals is rarely provided in machine-readable form, especially as Linked Data. In this paper, we describe the harvestingprocess, content and structure of the new dataset and its application in the Lin|gu|is|tik portal, a research platform for linguists. Asidefrom that, we introduce tools for the conversion of XML encoded language resources to the CoNLL format. The generated RDF data as
well as the XML-converter application are made public under an open license.
Keywords: LLOD, OLiA, CoNLL, Tiger-XML

1. Motivation

tab-separated column formats, a de-facto standard for annotated corpora as popularized as part of the CoNLL Shared
Tasks. In the paper we describe the extension of our analysis to text resources encoded in RDF and XML formats
thereby introducing tools that can be used for transforming
XML language resources into the CoNLL format.
Finally, we discuss how the harvested metadata is integrated into the Lin|gu|is|tik portal (Chiarcos et al., 2016), a
research platform for linguists funded by the DFG project
Fachinformationsdienst Linguistik (FID) and hosted at the
University Library Frankfurt. A special focus is put on ourcontinued efforts on mapping BLL language identifiers to
Glottolog and lexvo. Both, the Annohub RDF dump andthe BLL linking models are available at https://www.
linguistik.de/de/lod/. The XML-CoNLL converter application can be found at https://github.
com/acoli-repo/xml2conll.

Over the past decade, an ever growing amount of linguistic resources has become available on the web under anopen license. The Linguistic Linked Open Data (LLOD)
cloud1 currently encompasses not only annotated corporaand dictionaries, but also terminological repositories, ontologies and knowledge bases. However, despite the effortsof improving interoperability and interconnection of resources by using semantic web vocabularies and technologies, many resources are still heterogeneously annotatedand intransparently labeled with regard to their compatibility. This problem is by no means limited to LLOD but
applies to machine readable language resources in general.
Metadata repositories like META-SHARE2 , CLARIN centers3 or DataHub4 lack information about applicable annotation schemes. As for language metadata, language encoding standards vary across different metadata providers5 ,and in addition metadata is not always provided as linked
data.
With Annohub (annotation hub) we tackle this deficit bycreating a collection of languages and annotation schemes
used in existing language resources, and thus aim to augment existing metadata repositories. Annohub therefore
utilizes classification schemes supported by and linked tothe thesaurus of the Bibliography of Linguistic Literature(BLL).6 This encompasses the Ontologies of Linguistic
Annotation (OLiA) (Chiarcos and Sukhreva, 2015) and its
respective Linking Models for compatibility with a large
amount of linguistic annotation schemes (Dimitrova et al.,2016), and also Glottolog (Nordhoff and Hammarström,2011) and lexvo (de Melo, 2015) as supported language
identifiers (Dimitrova et al., 2018).
In previous work (Abromeit and Chiarcos, 2019) we focused on the analysis of language resources available in

2. Finding annotated language resources
Our premier source of metadata for existing language resources is the linghub RDF dump7 that contains over
200,000 linguistic resources. Additionally we query various CLARIN centers8 via the OAI protocol9 , but also
manually collect metadata from providers such as http://opus.nlpl.eu/ and others. All harvested resource
metadata is stored in a database which is used to keep trackof already processed resources. Of course, duplicate entries are a problem especially when a resource is available
at different locations. We did not tackle this problem in detail yet. It is planned, however, to subsequently integrate
new metadata entries, that we discovered, into the linghub
portal.

2.1.

Document classification

On the basis of the collected metadata, we identify language resources which could contain annotated text, suchas corpora, lexica and also linguistic metadata as found in

1

The LLOD cloud (http://linguistic-lod.org) is an integral part of
the general Linked Open Data cloud under https://www.lod-cloud.net
2
http://www.meta-share.eu and http://www.meta-net.eu
3
https://www.clarin.eu/
4
https://datahub.io
5
e.g. different ISO639 encodings, lexvo URLs or even plain text language descriptors
6
https://data.linguistik.de/bll/index.html

7

http://linghub.org/download
https://centres.clarin.eu/restxml/
9https://www.openarchives.org/OAI/
openarchivesprotocol.html
836

the Annohub web application with the respective download
URL or by csv file import. Additionally, processing can betriggered from the command-line interface.

3.

Extraction of annotation information

We are mainly dealing with annotations from the field ofsyntax and morphology. Word annotations (e.g. part-ofspeech tags) usually take the form of string tokens, whichhave been assigned to a word either automatically by a tool,or manually by a researcher. Alternatively, ontology classes(e.g. http://www.lexinfo.net/ontology/2.
0/lexinfo#adverb) typically found in RDF corpora
are used to annotate words. Feature annotations (e.g. number), as found in CoNLL data 14 , involve a feature name anda feature value (e.g. Number=Plur). Finally, dependency
information about the syntactic relation between words15
in a sentence is relevant to us. In order to extract annotations from texts, specific parsers for CoNLL, XML andRDF data have been developed.

3.1.

Figure 1: Annohub architecture
ontologies. For the identification of relevant files we rely on
standard techniques for content type detection like (html/mime) type, file extensions and also employ the Apache
Tika content analysis toolkit10 . A considerable amountof linguistic resources is provided in archive formats11 .
Archives can include large numbers of files and we observed archives with up to 100,000 files. Since processing all files in such cases would take too long, we sample
at maximum 100 files (depending on the file type) for thesubsequent analysis. Currently, we support RDF, CoNLL12
13, CoNLL-RDF (Chiarcos and Fäth, 2017) and XML type
documents with certain limitations (see chapter 6.1.)

2.2.

CoNLL annotation extraction

A CoNLL file has dedicated columns for e.g. part-ofspeech, morphological features and dependency information. However, since CoNLL is a format family with
distinct dialects mostly originating from specific CoNLL
shared tasks, the number of columns and also the orderingof columns in a CoNLL file is not standardized. Therefore,
first the columns storing POS-tag, feature and dependency
information have to be determined. For each such column
the set of occurring tags is fed into our model detection
component (see fig. 1).

3.2.

XML annotation extraction

In a preprocessing step we convert XML files to the CoNLL
format (see chapter 6.). Thus, the extraction process is thesame as for CoNLL files.

3.3.

RDF annotation extraction

Extracting linguistic annotations from RDF language
resources is a more complex task since such resources
often contain multiple types of annotations at the sametime, for example for syntax, semantics and pragmatics.
Another problem is that, although RDF vocabularies like
Lexinfo16 , NIF17 or OntoLex-Lemon18 exist which havebeen specifically designed to model syntax and morphology, researchers sometimes use their own proprietary RDF
vocabularies.
One way to implement the extraction process would be to
get the object values for selected RDF predicates typicallyDocument processing

Linghub resources are automatically processed by firstquerying the linghub RDF dump via SPARQL and thenfeeding RDF, CONLL, XML and archive resource types
into the Annohub NLP pipeline (see fig. 1). Since the automatic processing of language resources listed in CLARIN
centers is not implemented yet, we currently only use the
CLARIN resource metadata (e.g. author, title, etc.), that we
harvest via the OAI protocol to augment manually foundlanguage resources. These can be processed by means of

14
https://universaldependencies.org/u/
feat/index.html
15
https://universaldependencies.org/u/dep/
index.html
16
https://www.lexinfo.net/ontology/2.0/
lexinfo.owl
17
https://persistence.uni-leipzig.org/
nlp2rdf/ontologies/nif-core/nif-core.html
18
https://www.w3.org/2016/05/ontolex

10

https://tika.apache.org/
gzip,zip,tar,rar to name a few
12https://universaldependencies.org/
format.html
13
CoNLL-U, CoNLL-X and other TSV formats
11

37

used for annotating (e.g. http://www.lexinfo.
net/ontology/2.0/lexinfo#partOfSpeech)
via SPARQL queries19 from an RDF file. However, in
this approach the list of RDF predicates known to thealgorithm would limit the results. To avoid this limitation
we approach the extraction problem in a more generic
way by sampling object values from the set of all RDF
predicates that occur in an RDF file, (see algorithm 1) andcomparing them with the list of annotations defined in the
OLiA20 ontologies (Chiarcos and Sukhreva, 2015). The
OLiA ontologies provide a formalized, machine-readable
view on linguistic annotations for more than 75 different
language varieties. They cover morphology, morphosyntax, phrase structure syntax, dependency syntax, aspects ofsemantics, and recent extensions to discourse, information
structure and anaphora, all of these are linked with an
overarching reference terminology module. Furthermore
OLiA includes several multi-lingual or cross-linguisticallyapplicable annotation models such as the Universal Dependencies (77 languages), EAGLES (11 European languages)
and Multext-East (16 Eastern European and Near Eastern
languages).
Algorithm 1 RDF annotation detection
1: Input : The set of all different RDF predicates in a file
2: For each RDF predicate p :3:Take a sample of n object values o from triples(s p o)4:
If o is of type literal :5:
Try to match o against the set of known
annotation tags (in OLiA linking models)
6:
Else :
7:
Try to match o against the set of known
annotation classes (in OLiA annotation models)
8: Repeat the algorithm with the set of predicates withvalues that could be matched to tags or classes in OLiA
as input and omit in the second pass step 3 in order toretrieve all annotation matchings.

4.

Figure 2: Annohub RDF data modela resource is represented as a separate dataset. It contains
general information like file type, file size, etc. The analysis results of each file comprise the detected languages andidentified annotations from the field of syntax and morphology. By application of the Ontologies of Linguistic Annotations (OLiA) it is also possible to detect the annotation
schemes (tag sets) that were used.

Annohub dataset

The RDF data model for Annohub is depicted in fig. 2.
For modeling language resources, we utilize commonlyused RDF vocabularies including DCAT21 , Dublin Core 22 ,DCMI Metadata Terms23 and PROV24 . Each language resource is modeled as a DCAT dataset. It includes general
resource information like title, author, etc. that we harvest
automatically from the metadata providers linghub25 and
CLARIN26 . The metadata for language resources that werecollected manually was taken from the websites of the resource providers. Each annotated text file that comes with

4.1.

Annotation model analysis

The annotation model analysis is similar for all processed
document types. It is exemplarily described here for
CoNLL files.
Annotation model analysis for CoNLL files A CoNLL
file has dedicated columns for part-of-speech, morphological features and dependency information. Generally, foreach of these, a specific annotation model is used. By definition, a CoNLL column can contain annotations only froma single annotation model. A CoNLL file dataset therefore
may link to several annohub:AnnotationScheme instances,
but only to one for each column.

19

https://www.w3.org/TR/sparql11-overview/
http://purl.org/olia
21
https://www.w3.org/TR/vocab-dcat/
22
http://purl.org/dc/elements/1.1/
23
http://purl.org/dc/terms/
24
http://www.w3.org/ns/prov#
25
http://linghub.org/
26
https://centres.clarin.eu/restxml/
20

38

CoNLL model analysis results In the property annohub:column the considered column of a file is marked. The
information of what annotation scheme (e.g. UniversalDependencies-part-of-speech27 ) has been detected in acolumn is included with the annohub:isDefinedBy property.
Its value is a URL that points to an OLiA
linking model28 (e.g.
http://purl.org/olia/
ud-pos-link.rdf). This provides a mapping of all
identified tags to annotation classes in the OLiA ontology.
Part-of-speech annotations are highly ambiguous acrossdifferent annotation models. For example DT is equallyused in 6 different annotation models29 to declare a word
as Determiner. The algorithm used to determine a best fitting annotation scheme is fully described in (Abromeit andChiarcos, 2019) chapter 3 (Automated detection of annotation models). It is also used for RDF document types.
The results of this algorithm comprise several statisticalparameters that can be used to assess the quality of themodel analysis results which are provided in the annohub:annotationScheme class, and are described as follows.
This statistical data is also available in the Annohub editor application and is helpful for the verification of the detected annotation schemes. A detailed description of theeditor application is also presented in the above mentioned
publication.

using annohub:annotatedTag, annohub:annotatedClass orannohub:matchedClass. Finally annohub:hasBllConcept
provides a link to a class in the BLL ontology that is related to a word's annotation (see chapter 5.).

4.2.

Language analysis

The language analysis is different for each of the examineddata formats.
4.2.1. Language analysis for RDF files
In RDF files there are several sources for language information. First language tagged literals can provide an ISO639-1 (2-letter) code. Secondly language information canbe encoded with specific RDF predicates where the language info is a URL (e.g. http://lexvo.org/id/
iso639-3/nld) or a literal value that contains a ISO language code. We employ standard RDF predicates that are
widely used to encode language information taken from the
Dublin Core, OntoLex-Lemon and lexvo ontologies30 anddiscard cases where the language info is encoded as plain
text (e.g. English, Old (ca. 450-1100)). The extracted language information is finally normalized to a ISO639-3 representation.
RDF language analysis results All detected languages are encoded as lexvo-URL31 with the property
lexvo:language. Additionally the full (english) language
name taken from the ISO639-3 code table 32 is provided
in rdfs:label. In the property annohub:confidence a probability for a detected language is stored. Because the language info in RDF files is explicitly given as URL or ISO
code its value is 1.0. The property annohub:method stores
where the language info originates from. It’s a string constant which is LANGTAG for tagged literals and LANGPROP for languages that were encoded with a RDF property. The value of annohub:rdfProperty is simply p in the
RDF triple (s p o) where o stores language information.
That property can also be used to determine the type of a
resource (e.g. http://www.w3.org/ns/lemon/lime#
language) indicates a document of type lexicon). Finallyannohub:hasBllConcept links to a class in the BLL ontology33 that relates to a language (see also chapter 5.).

• annohub:exclusiveHitTypes is the number of different
tags that could be matched only by that model• annohub:hitTypes is the number of different tags that
could be matched• annohub:unmatchedTags is the number of unmatchedtags• annohub:coverage is the value of matched/unmatchedtags which is a float value (0-1)• annohub:count is the total number of occurrences ofmatched tags
• annohub:exclusiveHitCount is the number of occurrences of matched tags (but only for exclusiveHitTypes)

4.2.2. Language analysis for CoNLL files
In previous work (Abromeit and Chiarcos, 2019) we performed a quantitative analysis of the language detection
procedure for the CoNLL format. For the language detection we use the Optimaize34 n-gram based language classification tool. As input we choose a fixed number of k35

Furthermore the property annohub:method stores information on how the model assignment was achieved. It is ANNOMODEL by default and SELECTION if it was changed
manually by means of the Annohub editor.
All properties but annohub:column also apply to the model
analysis for RDF and XML files. For these file types instead annohub:rdfProperty and annohub:xmlAttribute areused to specify the location where an annotation was found.
Detailed information about each matched tag (or annotation URL, as found in RDF files) and its counterpart
in OLiA is stored in instances of annohub:Annotation by30
11 properties taken from the Dublin Core, OntoLexLemon and lexvo ontologies, e.g. http://purl.org/dc/
terms/language, http://www.w3.org/ns/lemon/lime#language,
http://lexvo.org/ontology#
iso639P3PCode)31
http://lexvo.org/ontology
32
https://iso639-3.sil.org/code_tables/
download_tables
33
https://data.linguistik.de34
https://github.com/optimaize/
language-detector
35
We choose k=15 for scalability reasons. Increasing k might
improve results but has not been tested

27
https://universaldependencies.org/u/pos/index.html
28
as documented at http://purl.org/olia
29e.g.
http://purl.org/olia/brown.owl#DT as
well as for the GENIA, QTag, Mamba and Penn Treebank annotation schemes

39

sample sentences from the WORD (LEMMA) column ina CoNLL file. Then we run the language classification oneach sentence and select the language as winner Lw which
was detected for the majority of sentences. The probability
Lp for a language is thenprocess. While the original thesaurus hierarchy is automatically exported in SKOS format on a regular basis, the BLL
ontology is manually assessed and updated in order to improve usability and interoperability in the context of LLOD.
Within the ontology, BLL index terms are rendered as OWLclasses and then linked to OLiA classes through subclassrelations.

#sample sentences where Lw was detected#sample sentences

5.2.

In situations where no majority winner was found one of
the best languages is randomly selected. For a discussion
on misclassification errors we refer to the mentioned evaluation paper above.

Recently, the scope of BLL ontology has been extended
to include the index terms for language identifiers anda linking to both Glottolog and lexvo. Our last release only included the language identifiers subsumed under the ontological class Indo-European language
identifiers of which approximately 60% could belinked to at least one LLOD repository (Dimitrova et al.,2018). For the current version we also include terminology
from non-Indo-European languages.

CoNLL language analysis results The detected WORD
(LEMMA) columns of a CoNLL file are stored with theannhub:column property. Languages are encoded in thesame manner as for RDF files where the language probability Lp is saved in annohub:confidence as a float value inthe range [0-1] and annohub:method describes how the language was determined. Its value is either LANGPROFILEin case the Optimaize tool provided the result or SELECTION if it was assigned manually by means of the Annohub
editor. In order to allow other language detection tools thedetection tool info is provided with prov:wasGeneratedBy
which is in this case the URL of the Optimaize languagedetector library. Finally annohub:hasBllConcept stores a
URL which is a class from the BLL ontology36 that is related to a language (see chapter 5. for more details).

FamilyIndo-Eur.
Other
Total

Integration of Annohub and the
Lin|gu|is|tik portal

6.

Glottolog
7
32
39

none
298
187
485

Total
645
1964
2609

Support for XML language resources

The XML formalism has been widely used for the representation of linguistic resources. This is partly due to the factthat NLP tools use this format for input and output. Also,
popular formats like TEI37 are built on it. Today, a substantial part of available corpora data is still only available in
XML formats38 although new Linked Open Data formats
have been introduced. We have built tools that can be used
to convert corpus data from XML to the CoNLL39 format
that is widely used in the community. Furthermore, for the

Linking of linguistic annotations

In earlier efforts of the DFG-funded series of projects(Chiarcos et al., 2016) we had created a linking of the BLLThesaurus with OLiA for linguistic terminology, mostly on
the field of syntax and morphosyntax. For this purpose the
BLL thesaurus had been transformed to RDF in a two-step
36

lexvo
104
416
520

Table 1 shows the current status of the linking which is,
however, an ongoing effort and thus subject to change.
While the status of the language identifiers in the IndoEuropean family is consistent with the last release, the nonIndo-European branch adds almost 2000 new classes ofwhich approximately 90% are linked to at least Glottolog
or lexvo. The higher coverage of links within this branchof the thesaurus stems from the BLL’s focus on European
languages. While the terminology on the Indo-European
branch is more granularly organized and comprises a comprehensive set of dialects, language families and historicalterms, the non-Indo-European branch mostly contains wellestablished identifiers for actual languages which have a
one-to-one correspondence in other repositories.
Both the revised version of the BLL ontology and thelanguage linking are available under https://www.
linguistik.de/de/lod/.

After harvesting linguistic resources of heterogeneous
sources and formats and extracting their annotations andlanguage information, an additional linking step is performed to make them not only available through Annohub
but also searchable and browsable within the Lin|gu|is|tik
portal. This process is relying on ontological links between
BLL index terms and other repositories for linguistic classifications, i.e. OLiA (Chiarcos and Sukhreva, 2015), Glottolog (Nordhoff and Hammarström, 2011) and lexvo (deMelo, 2015). In this chapter we give a brief overview on
how these links are established.

5.1.

both
236
1329
1565

Table 1: Linking of Language Identifiers

4.2.3. Language analysis for XML files
Since XML files are converted to CoNLL files before processing the only difference to the CoNLL language analysis
is in the annohub:xmlAttribute property. It stores a relative
XPath that describes the location of the word/lemma information in an XML document.

5.

Linking of language identifiers

37

https://github.com/TEIC/TEIExamples are http://opus.nlpl.eu/ and https://spraakbanken.gu.se/39
https://universaldependencies.org/
format.html
38https://data.linguistik.de

40

<?xml version="1.0" encoding="utf-8"?><document>

CoNLL format, tools for the conversion into the RDF format are available40 .

6.1.

<CHAPTER ID="1"><P id="1"><s id="1"><w tree="NC" lem="aprobación" id="w1.1">Aprobación</w><w tree="PDEL" lem="del" id="w1.2">del</w><w tree="NC" lem="acta" id="w1.3">Acta</w><w tree="PREP" lem="de" id="w1.4">de</w><w tree="ART" lem="el" id="w1.5">la</w><w tree="NC" lem="sesión" id="w1.6">sesión</w><w tree="ADJ" lem="anterior" id="w1.7">anterior</w>
</P><SPEAKER ID="1" NAME="La Presidenta"><P id="2"><s id="2"><w tree="ART" lem="el" id="w2.1">El</w><w tree="NC" lem="acta" id="w2.2">Acta</w><w tree="PREP" lem="de" id="w2.3">de</w>...

Supported XML formats for CoNLL
conversion

Stand-off annotation XML formats have annotations stored
in separate files from the text data (e.g. ATLAS (Bird andLiberman, 2001), PAULA XML (Dipper, 2005) or GrAF
(Ide and Suderman, 2007). We do not support these formats
at the moment because this would involve a reconstructionof the data. Moreover we focus on XML resources that include annotations and the raw text data together within asingle file like Tiger-XML (W. Lezius and Gerstenberger,
2002) does. In Tiger XML41 a graph element contains alist of terminal nodes that represent the words of a sentence. Each terminal element has word information likereference id, part-of-speech, morphological features, wordand lemma. However, there is no defined standard with respect to the naming of XML elements and attributes. In
practice, a class of XML documents can be identified thatshares the encoding formalism of Tiger-XML. For parsing
the XML several issues have to taken into account :Figure 3: Example XML file
document to a CoNLL column where this data should beplaced. In addition to this column mapping, a template
specifies the word nodes and the sentence boundaries in
the XML format. This has two consequences: firstly, theresulting CoNLL will have a valid separation of sentenceswith a newline. Secondly, the sentence boundaries allow
the XML to be streamed sequentially. Thus arbitrary XML
file sizes can be read, because a XML-DOM tree is only
created for each sentence and not the entire document.

1. XML documents can include optional elements that
store text structure information like chapter, paragraph, chunk
2. All names of XML elements and attributes are userdefinedSpecification of a template A template definition contains the following information:

3. All attributes of a terminal element (e.g. lemma, POS,etc.) are optional

1. id: An arbitrary identifier for the template. It is used
for the template matching algorithm
2. sentencePath: The name of the XML nodes that
contain a single sentence as their subtree

Instead of writing a specific XML parser for each documentclass we developed a template-based method that uses a description of a documents XML structure. This description(template) is then used by our XML parser application to
extract the annotated content.

6.2.

3. wordPath: XPath expression that will return a list ofnodes. Each column row will represent data relative toa single node in that list
4. columnPaths: XPath expressions relative to a single node contained in the list specified in 3. Each ofthese XPath expressions is assigned to a specific column in the resulting CoNLL

Template based XML-CoNLL conversion

With the template-based conversion we are able to perform
a lossless transformation from the XML to the CoNLL format. It can be applied, if the following requirements aremet:

5. description: Provide a description of this template, e.g. what corpus family this template was tailored to. Useful for debugging. (optional)

1. The XML represents the concept of a sentence
2. The XML represents the concept of a word/token

6. featurePaths: Identical to 4 in structure. Resulting values will be conflated into a single column withthe key1=value1|key2=value2 style CoNLLU uses for morphosyntactic features. (optional)

3. All word level annotations are represented as descendants of the word node
4. The name of the annotations are given as attributenames / the annotations are not reified.

Template matching algorithm In our processing workflow (fig.1) documents which have been classified as XML
documents are fed into our processing pipeline. However atthis point it is not clear if an XML document contains any
useful linguistic content. In order to rule out useless content we check the document’s XML structure against a setof given XML-conversion template definitions. Algorithm
2 computes a score that is then used to decide which XML

Intuitively, one writes a set of rules (template) in a JSON
file that is used as input together with an XML file togenerate CoNLL from it. A template provides a mappingfrom the data found at a (relative) XPath in the XML40

https://github.com/acoli-repo/conll-rdf
http://www.coli.uni-saarland.de/projects/salsa/salto/doc/html/node55.html
41

41

0
1
2
3
4
5
6

w1.1
w1.2
w1.3
w1.4
w1.5
w1.6
w1.7

aprobación
Aprobación
del
del
acta
Acta
de
de
el
la
sesiónsesiónanterior
anterior

0
1
2
...

w2.1
w2.2
w2.3

el
acta
de

to a scenario were a stream of XML documents enters ouranalysis pipeline where• the structure of XML documents is unknownEl• it can be assumed that many XML documents do notcontain useful content

Acta
de

• the amount of documents is much larger than for thetemplate-based approach

Figure 4: Resulting CoNLL for XML depicted in fig. 3{Generic matching algorithm The primary goal of algorithm 3 is to find the element in a XML document that
contains the most annotation information. As a first stepa list of all available XML nodes which are unique up
to a list index is computed 42 . In the following a example computation of the algorithm is described for the
XML source in fig.3. The results are depicted in table 2. A row in table 2 stores all relative XPath of one
unique XML node. In the first column the relative XPath,in the second column the value (attribute or text value)
and in the third column the score (matching annotations)are depicted. From the table it can be seen that only
the values of document/CHAPTER/P/s/w//@tree
could be matched with known annotations. Although only
the attribute document/CHAPTER/P/s/w//@tree
from elements document/CHAPTER/P/s//w could
be matched we conclude that also the other attributes
like document/CHAPTER/P/s/w//@lemma containword, lemma or dependency information.43 Hence we
extract all attribute and text values from XML elements
document/CHAPTER/P/s//w. These are then used tobuild a CoNLL file from it. Since we only use a small portion of the XML document for the detection process themethod can be used to quickly rule out XML documentsthat do not contain any annotation information at all. We
have found that taking 10 sample values (see algorithm 3
step 2) is sufficient for this purpose.

"id" : "8","sentencePath" : "s","wordPath" : "//w","columnPaths" : {"id" : "@id","lem" : "@lem","token" : "text()"}}

Figure 5: Template for XML in fig. 3 to produce CoNLL in
fig. 4
documents will be passed to the next processing stage (Language and Annotation Model detection). We define the best
fitting template for a given XML file as the template withthe highest recall score.
Algorithm 2 Template XML-CoNLL conversion
1: For each sample sentence s:2:Create the DOM tree d for s
3:
For each conversion template t:
4:Compare all attribute names found in dwith the attribute names specified
in the columnPaths field of t
5:
Calculate recall, precision and accuracy for t
6: Sort all templates t in descending order by recall, precision, accuracy
7: Output the top-most template as best matching template

Algorithm 3 Generic XML-CoNLL conversion
1: Generate the list of unique XML nodes
2: Take n sample values44 for each of the nodes in 1.
3: Filter nodes that have only numeric values
4: Compute a score which is simply the sum of all different values of one XML node that could be matchedagainst the set of known annotations
5: The XML node with the highest score provides themost annotation information. Extract all attribute andtext values from XML elements that are represented bya relative XPath for that node

Limitations The rule-based approach requires human input and is limited to the set of available templates. So it
cannot identify documents with an unknown XML structure. It is highly accurate and the best solution if a language resource needs to be converted that has many XML
documents that all share the same structure. It also can
produce acceptable results if the best matching template
is not a perfect match. Finally, we only support XML
dialects that do not reify their annotations: E.g. encoding the first lemma in fig. 3 as <w annotation="lem"value="aprobación"> would break the algorithm.

6.3.

42

A list of XML child nodes that have the same parent node isrepresented by a relative XPath43
In general, attribute names don’t have to be meaningful (likehere : lemma)44
XML attribute or text value

Generic XML-CoNLL conversion

In the following, we describe a algorithm that solves someof the problems of the template approach. It is well suited42Relative XPath for xml nodes
document
document/CHAPTER//@id
document/CHAPTER/P//@id
document/CHAPTER/P/s//@id
document/CHAPTER/P/s/w//@tree
document/CHAPTER/P/s/w//@lemdocument/CHAPTER/P/s/w//@id
document/CHAPTER/P/s/w//text()
document/CHAPTER/SPEAKER//@id
document/CHAPTER/SPEAKER//@name

Found values
1,2
1,2
1,2
ART, NCel,sesión1,2la,sesión1
La Presidenta

Score
0
0
0
0
2
0
0
0
0
0

that are published in the Annohub RDF dataset. Currently
the dataset contains 609 different language resources that
include 915 files. In table 4 the distribution for different
resource types is shown.
Resources

7. Results

Model found393 (25%)263 (100%)350 (73%)1006 (43%)

Ontology
4

Total
609

For future releases we are planning to extend this classification to other resource types like wordnets, etc.

So far 2317 files from 1508 resources have been processed.
Of these were 1572 RDF, 263 CoNLL and 482 XML files.
Table 3 below shows in how many files at least one language or annotation model could be detected.
Processed
1572
263
482
2317

Lexicon214

Table 4: Language resource classification

Table 2: Example computation for alg. 3File type
RDF
CoNLL
XML
Total

Corpus391

Resources

linghub.org
128

CLARIN
77

USER
404

Total
609

Table 5: Overview of metadata providers
Table 5 shows the metadata providers for resources included in Annohub. At the date of writing, the linghub 51
portal is updated with new resources that have been collected by members of the Prêt-à-LLOD 52 working group.
We are planning to include the results for these as well as
more results for language resources listed on CLARIN in
future releases.

Language found503 (32%)
254 (97%)375 (78%)1132 (49%)

Table 3: Results by file type8.

Obviously, for the CoNLL format the percentage of files
that yielded results is nearly 100%. This can be explainedby the fact that CoNLL files include usable data by default.
For the few cases were no language could be detected aparse error might be the cause for the error, or the language info was missing in the CoNLL data. A large portionof the processed XML files (~75%) could be converted to
the CoNLL format. Because most XML files were manually selected from language resource providers like http://opus.nlpl.eu/ and https://spraakbanken.
gu.se, this result is not unexpected and the error rate(~25%) can be explained with other XML files that were
included in archive resources (e.g. zip, tar) together withthe real data. On the other hand RDF resources were automatically collected and did not reveal as much usable data
as we expected. In fact the RDF format is commonly used
for semantic web data, perhaps even more than for LLOD
data. In total, 22 different annotation models and 3855 different languages could be identified. In addition to that, we
detected RDF namespaces for vocabularies of linguistic interest such as OLiA45 , GOLD46 , NIF47 , OntoLex-Lemon48 ,UBY49 and LexInfo50 . However, these are not included in
the Annohub RDF dump for now.
The results for files listed in table 3 are finally reviewed
by a linguist with the Annohub editor application. It is
used to correct wrong results and to select the resources

Summary

We introduced a new LLOD dataset which provides annotation model and language information for publicly available
language resources. By means of SPARQL queries this data
can be linked with other existing LLOD resources, as wehave shown by the use case of the Lin|gu|is|tik portal. Certainly, the availability of annotation metadata will enable
other new Linked Data applications. Finally, the provided
tools for the conversion of XML data to the CoNLL format
also contribute to the LLOD cloud since other converters53
for a later conversion to the CoNLL-RDF (Chiarcos andFäth, 2017) format exist.

9. Acknowledgements
The research described in this paper was conducted in
the context of the Specialized Information Service Linguistics (FID), funded by German Research Foundation(DFG/LIS, 2017-2019). The contributions of the second
author were conducted with additional support from the
Horizon 2020 Research and Innovation Action ‘Prêt-àLLOD’, Grant Agreement number 825182. The authors
would like to thank Christian Chiarcos for providing expert
advice throughout the project. We would also like to thankThorsten Fritze and Yunus Söyleyici for technical support
and Vanya Dimitrova for helpful comments.

10.

Bibliographical References

Abromeit, F. and Chiarcos, C. (2019). Automatic Detection of Language and Annotation Model Information in
CoNLL Corpora. In Maria Eskevich, et al., editors, 2nd
Conference on Language, Data and Knowledge (LDK45

http://purl.org/olia/olia.owl
http://purl.org/linguistics/gold47
http://persistence.uni-leipzig.org/
nlp2rdf/ontologies/nif-core#48
http://www.w3.org/ns/lemon/ontolex#
49
http://purl.org/olia/ubyCat.owl
50
http://www.lexinfo.net/ontology/2.0/
lexinfo
46

51

http://linghub.org
https://www.pret-a-llod.eu/
53
https://github.com/acoli-repo/conll-rdf
52

43

2019), volume 70 of OpenAccess Series in Informatics(OASIcs), pages 23:1–23:9, Dagstuhl, Germany. Schloss
Dagstuhl–Leibniz-Zentrum fuer Informatik.
Bird, S. and Liberman, M. (2001). A formal framework
for linguistic annotation. Speech Communication 33(12), pages 23–60.
Chiarcos, C. and Fäth, C. (2017). CoNLL-RDF: Linked
Corpora Done in an NLP-Friendly Way. In Language,
Data, and Knowledge - First International Conference,
LDK 2017, Galway, Ireland, June 19-20, 2017, Proceedings, pages 74–88.
Chiarcos, C. and Sukhreva, M. (2015). OLIA - Ontologies of Linguistic Annotation. Semantic Web Journal,
518:379–386.
Chiarcos, C., Fäth, C., Renner-Westermann, H., Abromeit,F., and Dimitrova, V. (2016). Lin|gu|is|tik: Building
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H., and Abromeit, F. (2016). Building an Ontological Model of the BLL Thesaurus: First Steps Towards
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50–58, Portorož, Slovenia, May. European Language Resources Association (ELRA).
Dimitrova, V., Fäth, C., Chiarcos, C., Renner-Westermann,
H., and Abromeit, F. (2018). Interoperability of
Language-related Information: Mapping the BLL Thesaurus to Lexvo and Glottolog. In Nicoletta Calzolari (Conference chair), et al., editors, Proceedings ofthe Eleventh International Conference on Language Resources and Evaluation (LREC 2018), Miyazaki, Japan,
May 7-12, 2018. European Language Resources Association (ELRA).
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44

Proceedings of the 7th Workshop on Linked Data in Linguistics (LDL-2020), pages 45–51
Language Resources and Evaluation Conference (LREC 2020), Marseille, 11–16 May 2020c European Language Resources Association (ELRA), licensed under CC-BY-NC

Challenges of Word Sense Alignment: Portuguese Language Resources
1

Ana Salgado1,2 , Sina Ahmadi3 , Alberto Simões4 , John McCrae3 , Rute Costa2

Academia das Ciências de Lisboa, Instituto de Lexicologia e Lexicografia da Lı́ngua Portuguesa, Lisbon, Portugal
2
NOVA CLUNL, Universidade NOVA de Lisboa, Lisbon, Portugal
3
Data Science Institute, National University of Ireland Galway
4
2Ai – School of Technology, IPCA, Barcelos, Portugal
anasalgado@campus.fcsh.unl.pt; {sina.ahmadi,john.mccrae}@insight-centre.org; rute.costa@fcsh.unl.pt; asimoes@ipca.pt
Abstract

This paper reports on an ongoing task of monolingual word sense alignment in which a comparative study between the Portuguese
Academy of Sciences Dictionary and the Dicionário Aberto is carried out in the context of the ELEXIS (European Lexicographic
Infrastructure) project. Word sense alignment involves searching for matching senses within dictionary entries of different lexical
resources and linking them, which poses significant challenges. The lexicographic criteria are not always entirely consistent within
individual dictionaries and even less so across different projects where different options may have been assumed in terms of structureand especially wording techniques of lexicographic glosses. This hinders the task of matching senses. We aim to present our annotation
workflow in Portuguese using the Semantic Web standards. The results obtained are useful for the discussion within the community.
Keywords: lexicography, sense alignment, linguistic linked data, Portuguese

1.

Introduction

glosses. It has been demonstrated that the task of WSA is
beneficial in many natural language processing (NLP) applications, particularly word sense disambiguation (Navigli
and Ponzetto, 2012) and information extraction (Moro etal., 2013).
In this paper, we are focused on the monolingual word
sense alignment (MWSA) task, which involves in sensealignment within two different resources in the same language. As an observer in the European Lexicographic
Infrastructure–ELEXIS3 (Krek et al., 2019; Declerck etal., 2018), the Academia das Ciências de Lisboa (ACL)
contributed to the task of MWSA in which the Portuguese Academy of Sciences Dictionary is compared toand aligned with the senses in the Dicionário Aberto. We
will report our experiences in annotating the senses withfour semantic relationships, namely, narrower, broader, exact and related. Representing the final data in the OntolexLemon model (McCrae et al., 2017), we believe that theoutcomes of this project will pave the way for further research on automatic WSA for the Portuguese language andenhance the accessibility of the data on the Semantic Web
and Linked Data.
The rest of the paper is organized as follows. In Section 2,
we introduce our Portuguese lexicographic resources andprovide a description of their content and structure. Section
3 summarises the methodology for annotation workflow. In
Section 4, we point out the major challenges of the MWSA
task for the Portuguese resources. We describe the conversion of the data into Ontolex-Lemon model in Section 5.
Finally, we conclude in Section 6 with a summary of ourcontributions.

The concept of the dictionary has changed with the adventof the world wide web (WWW) and the digital age. Theinteroperability of linked data technologies has played anessential role in the evolution of lexicography (Shadbolt etal., 2006; Heath and Bizer, 2011; Gracia et al., 2017). It has
been shown how lexicographic content can be represented
and connected dynamically, thus allowing us to abandon
once and for all the editorial perspective that still pervades
most digital resources which continue to mirror the structure used in the paper versions.
The use of semantic standards enables the organizationof vast amounts of lexical data in ontologies, Wordnets
and other machine-readable lexical resources resorting tonovel tools for the transformation and linking of multilingual datasets (McCrae and Declerck, 2019; Chiarcos et al.,2012). Linked Open Data (LLOD) promotes the use of the
RDF data model to publish lexical data on the web for a
global information system and interoperability issues.
There have been many efforts underway on behalf of numerous researchers to align different lexical resources (e.g.
(Navigli, 2006; Knight and Luk, 1994) dealing with theword sense alignment (WSA) task. We define this task aslinking a list of pairs of senses from two or more lexical
resources using semantic relationships. To mention a few
previous projects, Meyer and Gurevych (2011) align the
Princeton WordNet with the English Wiktionary1 , and Henrich et al. (2012) link the GermaNet–the German Wordnet
with the German Wikipedia2 .
WSA involves searching for matching senses within dictionary entries of different lexical resources and linking them,which poses significant challenges. The lexicographic criteria are not always entirely consistent within individual
dictionaries and even less so across different projects where
different options may have been assumed in terms of structure and especially wording techniques of lexicographic
1
2

2.

Lexicographic data

In the scope of ELEXIS, one of the main purposes is to
extract, structure and link multilingual lexicographic resources. One of the tasks to achieve this goal consists
3This project aims to create a European network of lexical resources (http://www.elex.is).

https://en.wiktionary.org
https://de.wikipedia.org

45

2.2.

of word sense alignment manual task in several languages(Ahmadi et al., 2020).The datasets are publicly freely available4 .The first established task is to provide semantic relations, as we will demonstrate in Section 3.

2.1.

Formats

Concerning formats, both Portuguese language resources
are available in printed editions and XML versions.
The DLPC was published in a two-volume paper version,
the first volume from A to F and the second from G to
Z, in a total of 3880 pages. This dictionary, available inprint and as a PDF document, was converted into XML using a slightly customized version of the P5 schema of the
Text Encoding Initiative (TEI) (Simões et al., 2016). The
XML was generated based on the dictionary PDF file, fromwhich most of the information on the microstructure wasrecovered automatically. The new ongoing digital edition,
DACL, is only privately available and has been edited with
LeXmart (Simões et al., 2019). At the same time, the dictionary is being converted to the TEI Lex-0 format (Salgado
et al., 2019b), a streamlined version of the TEI Dictionary
Chapter. The present work, therefore, had this digital version as a starting point.
Regarding the DA, the paper version comprises 2133 pages.
Currently, the dictionary is available online. Unlike DLPC,DA was transcribed manually by volunteers. This task required that the annotation format would be easy to learn,
but also, that it would be similar to the format used in
the transcription of other books for the Project Gutenberg6 .
Therefore, entries were only annotated with changes of font
types, i.e., italics and bold, and not semantic tags. Although
the dictionary is also available in XML, following the general guidelines of the Dictionary Chapter of TEI, the annotation granularity is bigger than DLPC. Specific portions of
the microstructure were easy to annotate. Consider, for example, the grammatical information, geographic variant, or
the knowledge domain. These entities are from a controlled
list of vocabulary, and after creating the list it was straightforward to annotate them. For the construction of these lists
we used the tables from the front-matter of the dictionary.
Nevertheless, as these lists were manually generated, they
were completed by performing dummy runs of the tagging
algorithm, and finding out parts of the entries that were notdetected. For other situations, like the annotation of usage
examples, or to distinguish between two different senses,
there are no clear marks to allow an algorithm to performthat automatically. While some hints could help, a good annotation would require manual validation. Under DA every
line in the definition element tag can be a different sense,
but can also be a usage example or even the continuation of
the previous sense definition (Simões et al., 2012). To correctly detect other parts of the microstructure would require
further manual revision that was not possible at that time.
Further developments on both dictionaries are programmed
as soon as funding is available.

DLPC and DA

For the completion of this task, we align the following two
Portuguese dictionaries:• the Dicionário da Lı́ngua Portuguesa Contemporânea(DLPC) (Academia das Ciências de Lisboa, 2001),
with the seal of ACL, coordinated by Malaca
Casteleiro and published in 2001, with the financial
support of the Calouste Gulbenkian Foundation, under the commercial responsibility of Editorial Verbo.
This dictionary also represents the first complete edition of a Portuguese Academy dictionary, from A to Z
(previous attempts in 1793 and 1976 did not go further
than the letter A). The DLPC contains around 70,000
entries. In 2015, some preparatory work for an online
Portuguese Academy of Science Dictionary (DACL)
was performed through the Instituto de Lexicologia
e Lexicografia da Lı́ngua Portuguesa (ILLLP) and a
database was developed by a team working in Natural Language Processing at the University of Minho,
which now draws on the participation of IPCA andNOVA CLUNL5 . The present work, therefore, had theretro-digitised version of DLPC as a starting point.
• the Dicionário Aberto (DA) (Simões and Farinha,2010), a Portuguese language dictionary obtained by
the full transcription of Nôvo Diccionário da Lı́ngua
Portuguêsa, authored by Cândido de Figueiredo, andpublished in 1913 by Livraria Clássica. Having the1913 edition entered the public domain, it was digitised and text-converted by a team of distributed proofreaders volunteers between 2007 and 2010 and wasmade publicly available on the Gutenberg Project
website on 8 March 2010. During the transcription
process, and as entries got reviewed, and therefore,
considered final, they were made freely available on
the web. For three years, the dictionary has expanded
by including more transcribed entries. After the complete transcription, the dictionary was subject to automatic orthography update and was used for different experiments regarding NLP tasks, as the automatic
extraction of information for the creation of Wordnets or ontologies (Gonçalo Oliveira, 2018; Oliveiraand Gomes, 2014). The updated version of the dictionary is available under license CC-BY-SA 2.5 PT. The
DA contains 128,521 entries. Although the numberof entries seems high, it is necessary to bear in mind
that this resource registers orthographic variants of thesame entry as we will mention later.

2.3.

Micro-structure analysis

The DLPC’s micro-structure is more complex than theDA’s, with more structured and hierarchical information.
Both dictionaries follow lexicographic conventions such as
bold type in headwords. Nevertheless, comparing the sample of entries, we may observe certain typographic differences: ACL features initial lowercase entries while the DA

4https://github.com/elexis-eu/MWSA
The team works with Alberto Simões (IPCA) and José João
Almeida (Natural Language Processing of the Computer Science
Department), and the consultancy of Álvaro Iriarte Sanromán.
The participation of NOVA CLUNL is related to the DACL’s transition into the TEI Lex-0 format.
5

6

46

https://www.gutenberg.org/ebooks/31552

Headword (POS) DLPC sensebanco (s. m.)

Semantic relation

Assento estreito e comprido, dematerial variável, com ou sem encosto,
relatedpara várias pessoas.
banco dos réus. 1. Lugar destinado aosréus, no tribunal. 2. Situação em que senoneé objecto de acusação em tribunal.

Sense match

DA sense

Assento, geralmente
tosco, de ferro,
madeira ou pedra, e deformas variadas.

Assento, geralmente
tosco, de ferro, madeiraou pedra, e de formas
variadas.

Escabelo.

Assento para uma pessoa, sem encosto,
de tampo redondo ou quadrado,sustentado por três ou quatro pés. ≈relatedmocho.

Assento, geralmente
tosco, de ferro,
madeira ou pedra, e deformas variadas.

Mesa estreita eoblonga, sobre que
trabalham certos
artífices.

Assento comprido e largo, com encostoalto, de tampo amovível, que podeservir também de tampa de uma arca. ≈exact
arquibanco, escabelo, escano.

Escabelo.

Balcão de comércio.

Figure 1: An example spreadsheet used for the annotation task.
has capitalized entries. Furthermore, only the DLPC provides full pronunciation information. The DLPC etymological information figures after the grammatical propertiesof the lexical item while, in the DA, such information appears at the end of the entry. While the DLPC indicates thepart-of-speech and gender, the DA displays the gender inthe case of nouns7 . One of the main features of the DLPC
is the split of entries. Not only etymological homonyms
are treated as independent entries, but also homonyms of
the same etymological family belonging to different partof-speech are differentiated by numeric superscripts to theright of the lemma in order to distinguish the respective entries (e.g. perfurador can function as an adjective, or a nounso is split into two entries).
Regarding the structure, the senses are numbered in the
DLPC, providing better organised and more fine-grained
information, while in the DA only a paragraph distinguishes
the different senses. This was the result of the lack of metadata added to the dictionary during the transcription process. Nevertheless, the dictionary has the basic microstructure annotated, including grammatical information, definitions, quotations, usage examples and etymological information. The DLPC has, in general, more structured information such as synonyms (preceded by ≈), examples(shown in italics), cross-reference to lexical units that preferentially co-occur are represented by the symbol +, usagelabelling, among other relevant features.
In the next section, we will explain in more detail how the
workflow annotation took place. The data was deliveredin XLM files and in an Excel format where the data wasconverted into spreadsheets.

3.

• The lemma is a “lexical unit chosen according tolexicographical conventions to represent the different
forms of an inflection paradigm” (ISO, 2007).
• A sense is one of the possible meanings or interpretations in a specific context.
• A gloss is a textual description of a sense’s meaning
meant for human interpretation.

3.1.

Entries selection

The selection of entries took into account some points
previously defined by the ELEXIS team (Ahmadi et al.,2020), namely: all open class words should be represented;
monosemous and polysemous lemmas should appear; and,
finally, the lemmas of both resources must had the same
part-of-speech. Taking these points into account, we decided to select isolated lemmas randomly and also selectdata sets followed alphabetically. As a sample of entries,
we chose:
A. random entries as long as they appeared in both dictionaries: banco [bank], bandarilha [banderilla], café[coffee], computador [computer], coração [heart], dicionário [dictionary], futebol [football], lexicografia[lexicography], mililitro [milliliter], praia [beach],sorridente [smiling] and tripeiro [tripe seller and native of Porto].
B. all the lexical items that came up between especial[special] and esperanto [Esperanto], perfume [perfume] and perlimpimpim [a lexical unit used in a fixed
combination pós de perlimpimpim [magical powder],a sequence of units sorted alphabetically from letters
E and P.

Methodology

In the previous two sections, we have presented the resources we decided to analyze and pointed out that they
have very different features. Before we move to the annotation workflow, we would like to define some of the terms
used in this particular task:

The total number of entries collected is 146 containing 786
distinct senses (8301 tokens).
After selecting the sample entries, we created dynamic
spreadsheets as the means of the annotation task (Figure 1). This sheet contains the following information:

7
This a common lexicographic practice: when it is marked asm. (masculine), it is understood that the lemma is a noun.

47

headwords (DLPC and DA lemmas identification); partof-speech (DLPC POS); senses in DLPC (DLPC senses);semantic relation; sense match (DA equivalent sense);
part-of-speech (DA POS); and, finally, senses in DA (DAsenses).

3.2.

~ Entrada - - - - - - - - - - - - - - - - - - - - . .

Periphrásticoadj.
Relativo à perífrase.
(Gr. periphrastikos)

Annotation workflow

Entrada - - - - - - - - - - - - - - - - - . . .

The annotation task was carried out fully manually. Givena lemma, corresponding senses in both dictionaries, the DA
and DLPC, were brought together in the spreadsheets. Thisway, all the possible combinations of the senses across thetwo resources were provided to the annotator. Unlike regular dictionaries, where a limited number of semantic relationships are defined, such as synonymy and antonymy,
we considered a broader range of semantic relationships,
namely the followings:

Perifrásticoadj.
Relativo à perífrase.
(Gr. periphrastikos)

Figure 2: periphrástico [periphrastic] and perifrástico [periphrastic] in DA
mililitro [mililítru]. s. m. (De mili- 1 + litro). Unidade de• exact: the two senses are semantically equivalent;

medida de capacidade (símb. ml) equivalente à milésima
parte do litro.

• narrower: the sense in DLPC describes a narrower
concept than that in the DA;

Entrada - - - - - - - - - - - - - - - - - - . . . .

Mi IiIitro• broader: the sense in DLPC describes a broader
concept than that in the DA;m.
A milésima parte do litro.
(De mi/li... + litro)• related: there is a possible alignment, detecting a
possible related relationship.

Figure 3: mililitro [milliliter] in DLPC (above) and DA (below)

In the case where no semantic relationship is found for asense, none is selected. Note that not all the semantic relationships are symmetric; therefore, the order of the columns
determines the relationship. We matched the senses of thetwo dictionaries, using the label corresponding to the properties cited above. The result is a mapping between senses.
In overall, 463 and 323 senses are aligned in the DLPC andDA, respectively. Among the whole number of 275 aligned
senses, 207 exact, 38 narrower, 28 related and 2 broader areprovided.

4.

forms would never appear in the DA and were not usefulfor the ongoing task8 .
Since we do not intend to discuss the wording techniquesof the gloss, we can say that between certain lexical items
senses, there is an exact correspondence of sense. There arecases where we can establish an exact relation between thesenses even in structural terms (see, mililitro [millilitre] thathas only one sense in both dictionaries, i.e., one-thousandthof a litre). However, these easily solvable cases are not what
we mostly encounter when dealing with different dictionaries (Figure 3).
There are several other cases where there are exact relations, but there are other senses that appear in only oneof the dictionaries. In Figure 4, DLPC sense 1 related tothe bullfighting domain [banderilla] corresponds to the only
sense of the DA. Sense 2 related to the bookbinding domain
only appears in the DLPC.
Nevertheless, and although the first sense is identical inboth resources, the disallowance is not identical in textual terms, since the meaning is described differently. The

Challenges of MWSA

We now move on to the challenges of WSA. When we firstchose these two lexicographic resources, we knew that wewould be dealing with a significant time lag: the DLPC was
published in 2001, and the DA in 1913. In 88 years, the Portuguese lexicon and language undergone many transformations: a Portuguese spelling reform, semantic changes ofthe lexical items (computador [computer], for example, inthe DA, is not defined as an electronic device, new words
have appeared, such as futebol [football], which is not included in the DA). All these factors are obstacles to thesuccessful performance of this task.
The Portuguese spelling has also changed. In the DA, theirdevelopment team decided to maintain old spelling variants, e.g. periphrástico and perifrástico (Figure 2), thus
enabling the search of all the orthographic variants.
For this task, we have ignored the old orthographic variant
forms of a given lexical unit, as they are present in duplicate in DA (with an updated version of the form). Since
the DLPC is a contemporary dictionary, these orthographic

8
From the DA XML file, we ignored the following entries: perhydrol, perianthado, periântheo, periânthio, periantho, periappendicite, perichécio, perichôndrio, perichondrite,
perichondrio, pericoróllia, pericyclo, pericystite, perididymite,
peridı́dymo, perı́dyo, perı́grapho, perigynândrio, perigynadro,
perigynia, perı́gyno, perimı́sio, perimorphose, perinephrite, periophthalmia, periorthógono, periosteóphyto, peripheria, periphérico, periphorantho, perı́phoro, perı́phrase, periphrástico,
peripyema, peristáchio, peristéthio, peristýlico, perissýstole,
perithécio, perityphlite.

48

DLPC also uses a domain label, “Taurom.” while in theDA, there is no label.

tripeiro 1 [triptjru]. adj. m. ef (De tripa+ suf. -eiro). Pop.
O m. que portuense1 .
tripeiro2 [triptjru]. s. m. ef (De tripa+ suf. -eiro). 1. Pessoa que vende tripas. 2. Pop. O m. que portuense2.

bandarilha [bBdBrÍÀB]. s. f

(Do cast. banderilla). 1. Taurom. Haste munida de ponta de metal penetrante, enfei-

Entradatada com uma bandeira ou com fitas de papel de cores eque se espeta no cachaço dos touros, durante a corrida. =:::

Tripeiro

FARPA, FERRO. A elegância com que espetou o par de banda-m.

rilhas no touro pôs a praça de pé. <<Abrem-se então as portas
e a manada entra, esta que será toureada hoje consoante os
preceitos inteiros da arte, passada à capa, espetada de bandarilhas, castigada de varas>> (SAAGO, Levantado doChão, p. 165). Cravar, espetar as +s; um par de +s; tércio
de +s. bandarilhas a quarteio, variedade de farpas em

Vendedor de tripas.
Aquele que se sustenta de tripas.
Deprec.
Habitante do Porto.
(De tripa)

que o toureiro faz um quarto de volta ao espetá-la no
touro. bandarilhas a recorte, movimento que consiste
em colocar os ferros no touro no momento em que otoureiro evita a marrada. 2. Encad. Tira de papel que secola na margem de um original ou prova, quando as
emendas não cabem nas margens.

Figure 5: tripeiro [tripe seller and native of Porto] in DLPC
(above) and DA (below)
pergamináceo, a [pirgeminásju, -B] . adj. (Do 6. lar. pergaminum ' pergaminho' + suf. -áceo) . I. Que se assemelha
ao pergaminho; que, p elo seu asp ec to, faz lembrar essa
p ele. ~ PERGAMIN HÁCEO. 2. Que é feito de pergaminho.

EntradaBandarilhaf.
Farpa, enfeitada com bandeiras ou fitas, e destinada a cravar-se no cachaço dos
toiros, quando se correm.
(Por bandeirilha, cast. banderilla)

pergaminháceo, a [pirgBmip ásju, -B] . adj. (De pergaminho + suf. -áceo) . O m. que pergamináceo.
EntradaPergamináceo

Figure 4: bandarilha [banderilla] in DLPC (above) and DA(below)adj.
,
O mesmo ou melhor que pergaminháceo. Cf. Arn. Gama, Ult. Dona, 55.

In other cases, the correspondence of senses is evident, but
the lexicographic criteria adopted differ as shown in Figure 5. The structure of these lexicographic articles is different. The DLPC has two entries for tripeiro (tripeiro1
and tripeiro2 ) as an adjective and a noun, part-of-speechhomonyms. The first entry is an adjective, and the second
is a noun; the DA has only one entry and only gender information. Between tripeiro2 (DLPC) and tripeiro (DA),there is an exact match in the first sense, an obsolete sense,
as a tripe seller although the technique of writing the glossdiffers (“Pessoa que vende tripas” [Person who sells tripes]
in DLPC and “Vendedor de tripas” [Tripe seller]) in DA.
These two glosses point to the same concept. However, although the DA did not record sense numbers, the first twosenses could be divided. We can established a match between sense two that start with “pop.” [popular] in DLPCand “Deprec.” [depreciative] in DA, another tricky topic is
usage information. This topic is related to the various typesof inconsistencies regarding usage labelling (Salgado et al.,2019a). Anyway, the only difference is that DLPC uses a
cross-reference, and the DA provides the gloss.
Other times, the senses are exact correspondences, but theeditorial perspective is different as shown in the example of
Figure 6: for pergamináceo [pergameneous] (DLPC), theDA presents a gloss and the DLPC a cross-reference. On
the other hand, pergiminháceo (DA) has a cross-reference
pergamináceo.
The DA, as mentioned above, does not use numbers forsenses. Thus, we have considered each paragraph as an independent sense. However, a DLPC sense may correspondto more than one DA sense. See praia [beach] entry in thesense of “Beira-mar” [seaside] (Figure 7).

EntradaPergaminháceo
adj.
Que tem o aspecto de pergaminho: << ••• os museu/os... pergaminháceos... >> Camilo,Volcões, 154.

Figure 6: pergamináceo/pergaminháceo [pergameneous]in DLPC (above) and DA (below)
In the DA (Figure 7), the senses “Beira-mar” [seaside]and “Região, banhada pelo mar; litoral; margem” [Region, bathed by the sea; coast] correspond to sense 2 of
the DLPC: “Zona banhada pelo mar; zona balnear” [Zonebathed by the sea; bathing area].
The same can be said, for example, of especial [special],
whose DLPC gloss, “Que tem, dadas as caracterı́sticas,
uma finalidade ou um uso particular. ≈ adequado, especı́fico, próprio. 6= geral.” [Which has, given the characteristics, a purpose or a particular use. ≈ suitable, specific, own], may correspond to three paragraphs of the DA:“Próprio. / Peculiar. / Particular.” [Own. / Peculiar. / Particular.].
Looking at the three glosses of banco [stool/bench] as “assento” [seat] in the DLPC:• “Assento estreito e comprido, de material variável,
com ou sem encosto, para várias pessoas.” [Narrowand long seat, of variable material, with or withoutbackrest, for several people.]• “Assento para uma pessoa, sem encosto, de tampo re-49

5. Data Conversion

praia [prájB] . s. f (Do lat. tardio plagia, t alvez do gr.
n Ààytoç 'oblíquo'). 1. Faixa arenosa do litoral m arítimo,
de fraca inclinação, muito utilizada por banhistas nas zonas de veraneio ou em estâncias de turismo. <<e a débil pegada que o meu obscuro pé imprimiu nas praias do Mindelo
há-de ficar gravada na história>> (GARRETT, D iscursos, p.
12 1). casa+ de praia. colchão+ de praia. voleibol+ depraia. 2. Zona banhada pelo mar; zona balnear. ~ BEIRA-MAR, COSTA, LITORAL. Passaram as férias na praia.

In order to increase the interoperability of the annotateddata with other language resources, we convert the final
datasets into the Ontolex-Lemon model (McCrae et al.,2017). This model provides rich linguistic groundingsfor ontologies which enables various representations suchas morphology and syntax. Our final output provides
the headword, the part-of-speech tag along with thesenses for each entry. Therefore, the following propertiesare respectively used:ontolex:writtenRep,lexinfo:partOfSpeech andskos:definition. Linking between the senses ismade with the SKOS matching properties. An example ofthis data in Turtle is given below:

EntradaPraia
f.
Orla de terra, geralmente coberta de areia, confinando com o mar.
Beiramar.
Região, banhada pelo mar; litoral; margem.
PI. Marn.

Depósito geral das águas que alimentam a salina, e que também se chama loiças, (cp.
loiça).
(Do lat. plaga)<#banco_noun> a ontolex:LexicalEntry ;rdfs:label "banco"@pt ;ontolex:sense <#sense0>, <#sense12>,<#sense13> .

Figure 7: praia [beach] in DLPC (above) and DA (below)<#sense0> skos:definiton"Assento estreito e comprido, dematerial variável, com ou sem encosto,
para várias pessoas. "@pt .

dondo ou quadrado, sustentado por três ou quatro pés.
≈ mocho.” [One person seat, without backrest, withround or square top, supported by three or four feet;stool]<#sense12> skos:definition"banco dos réus. 1. Lugar destinado
aos réus, no tribunal. 2. Situaçãoem que se é objecto de acusaçãoem tribunal."@pt .

• “Assento comprido e largo, com encosto alto, detampo amovı́vel, que pode servir também de tampa deuma arca. ≈ arquibanco, escabelo, escano.)” [Longand wide seat, with high back, removable top, which
can also serve as a chest lid. ≈ bench cabinet; bench.]<#sense0> skos:relatedMatch <#sense1> .
<#sense95> skos:exactMatch <#sense96> .
<#sense97> skos:narrowMatch <#sense96> .

It is tough to ascertain whether it is possible to make acorrespondence with the first sense of the DA, also thisone related to a seat: “Assento, geralmente tosco, de ferro,
madeira ou pedra, e de formas variadas.” [Seat, usuallyrough, of iron, wood or stone, and of different shapes.]
The last sense of the DLPC is a synonym of “escabelo”(also in the DA, so this is an “exact” correspondence), but
it may also be associated with the first sense of the DLPC.
Let us now turn to the lexicografia [lexicography] entry in
the DLPC:

The data is publicly available as part of the MWSA benchmark at https://github.com/elexis-eu/MWSA.

6.

Conclusion

This paper focuses on the task of monolingual word sensealignment for the Portuguese language. Focusing on
two lexicographic resources in Portuguese, namely, Dicionário da Lı́ngua Portuguesa Contemporânea and Dicionário Aberto, we presented the challenges and difficulties to manually align senses and annotate their semantic relationships. In addition, we also describe the conversion of
our aligned data into the Ontolex-Lemon model which improves interoperability and accessibility within the Linked
Data and Semantic Web technologies. We believe that ourdataset is beneficial to create tools and techniques to automatically align senses within Portuguese lexicographic resources. Moreover,• “Ling. Ramo da linguı́stica que se ocupa dos aspectos teóricos e práticos que têm em vista a elaboraçãode dicionários, vocabulários, glossários.” [Branch of
linguistics that deals with the theoretical and practicalaspects that aim to develop dictionaries, vocabularies,
glossaries.]
The same entry in DA, it is described as:• “Ciência ou estudo, que tem por objecto as palavras
que devem constituir um léxico.” [Science or study,
whose object is the words that must constitute a lexicon.]

7.

Acknowledgements

The authors would like to thank the anonymous reviewers for their thoughtful comments towards improving ourmanuscript. Research financed by Portuguese National
Funding through the FCT – Fundação para a Ciência e Tecnologia as part of the project Centro de Linguı́stica da Universidade NOVA de Lisboa – UID/LIN/03213/2020, by theEuropean Union’s Horizon 2020 research and innovation

Although the gloss differs (we intend to explore the issueof definition in more detail in future work), in these cases,
we always attribute an exact relationship since both refer tothe same concept.

50

program under Grant Agreement No. 731015 (ELEXIS),
and through the FCT/MCTES as part of the project 2Ai –School of Technology, IPCA – UIDB/05549/2020.

8.

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Simões, A., Almeida, J. J., and Salgado, A. (2016). Building a dictionary using xml technology. In 5th Symposium on Languages, Applications and Technologies(SLATE’16). Schloss Dagstuhl-Leibniz-Zentrum fuer Informatik.
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Lipp, V., Váradi, T., Simon, L., Győrffy, A., Tiberius, C.,
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Ureña-Ruiz, R.-J., Simov, K., Osenova, P., Kancheva, Z.,
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(2020). A Multilingual Evaluation Dataset for Monolingual Word Sense Alignment. In Proceedings of the 12th
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McCrae, J. P. and Declerck, T. (2019). Linguistic Linked
Open Data for All. In Proceedings of the Language Technology 4 All Conference.
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Academia das Ciências de Lisboa. (2001). Dicionárioda Lı́ngua Portuguesa Contemporânea. João Malaca
Casteleiro (ed.). Lisboa. Academia das Ciências de Lisboa e Editorial Verbo.
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Proceedings of the 7th Workshop on Linked Data in Linguistics (LDL-2020), pages 52–60
Language Resources and Evaluation Conference (LREC 2020), Marseille, 11–16 May 2020c European Language Resources Association (ELRA), licensed under CC-BY-NC

A Lime-Flavored REST API for Alignment Services
Manuel Fiorelli, Armando Stellato
University of Rome “Tor Vergata”, Department of Enterprise Engineering, via del Politecnico 1, 00133 Roma, Italy
fiorelli@info.uniroma2.it, stellato@uniroma2.it
Abstract
A practical alignment service should be flexible enough to handle the varied alignment scenarios that arise in the real world, while
minimizing the need for manual configuration. MAPLE, an orchestration framework for ontology alignment, supports this goal bycoordinating a few loosely coupled actors, which communicate and cooperate to solve a matching task using explicit metadata about the
input ontologies, other available resources and the task itself. The alignment task is thus summarized by a report listing its characteristics
and suggesting alignment strategies. The schema of the report is based on several metadata vocabularies, among which the Lime moduleof the OntoLex-Lemon model is particularly important, summarizing the lexical content of the input ontologies and describing externallanguage resources that may be exploited for performing the alignment. In this paper, we propose a REST API that enables theparticipation of downstream alignment services in the process orchestrated by MAPLE, helping them self-adapt in order to handle
heterogeneous alignment tasks and scenarios. The realization of this alignment orchestration effort has been performed through two main
phases: we first described its API as an OpenAPI specification (a la API-first), which we then exploited to generate server stubs andcompliant client libraries. Finally, we switched our focus to the integration of existing alignment systems, with one fully integrated
system and an additional one being worked on, in the effort to propose the API as a valuable addendum to any system being developed.
Keywords: Lime, OntoLex, VocBench, MAPLE, Ontology Matching

1.

service, meaning a web service for the computation ofalignments between datasets (in general). The contributionof this paper is precisely a REST API (Fielding, 2000) thatan alignment service shall implement in order to comply
with MAPLE. We used the OpenAPI7 format to describe
this API explicitly, ensuring that the produced
specifications are both machine-readable and humanfriendly. These specifications establish a contract that makeit possible for a user to invoke any alignment system forwhich a compliant server has been developed. We validated
our work through the implementation of a sever for onealignment system, while planning an analogous one for an
additional system.

Introduction

Ontology matching (Euzenat & Shvaiko, 2013) is the taskof computing an alignment between two (or more)
ontologies that consists of correspondences between
semantically related concepts. We consider a broader
definition of the task, to cover thesauri and datasets, ingeneral. We argued (Fiorelli et al., 2019) that a practicalmatching system should be flexible enough to recognize
different matching scenarios and handle each of them witha suitable strategy possibly benefiting from additional
support resources. Our framework MAPLE1 achieves thatgoal by exploiting explicit metadata about the input
ontologies and other available resources. MAPLE uses a
combination of metadata vocabularies, including DCMI
Metadata Terms2, FOAF3, VoID4, DCAT5 and Lime
(Fiorelli et al., 2015). The latter is the metadata module of
the OntoLex-Lemon model6 (McCrae et al., 2017;
Cimiano, McCrae, & Buitelaar, 2016), which is becominga cornerstone of the growing Linguistic Linked Open Data
cloud (Chiarcos, Nordhoff, & Hellmann, 2012), moving
beyond its original focus on ontology lexicons. MAPLE
uses Lime metadata to determine how lexical informationis represented (i.e. the so-called lexicalization model), thedegree of linguistic compatibility of the input ontologies(e.g. supported natural languages, relative coverage,
relative expressiveness, etc.), as well to find suitablelanguage resources (e.g. a wordnet) in some natural
language to support synonym expansion. MAPLE compiles
a task report that summarizes the characteristics of thegiven matching scenario and hints at possible matchingstrategies. This task report is intended to help a downstream
matching system configure itself in order to manage thegiven matching scenario as best as possible. In this paper,
we will refer to such a matching system as an alignment2.
2.1

Background

LIME: Linguistic Metadata

LIME is the module of OntoLex-Lemon dedicated to thedescription of lexicalized datasets and language resources
such as wordnets. LIME extends VoID, by defining
subclasses of void:Dataset based on the different roles thatthese datasets play form the view point of the ontologylexicon interface.
A lime:LexicalizationSet is a dataset consisting oflexicalizations for a given reference dataset in some natural
language, optionally using a lexicon, and expressed using
a specific lexicalization model. A lexicalization set can
describe the fact that an ontology (the reference dataset)contains RDFS labels (hence, RDFS is the lexicalization
model) in English. If the ontology also contains labels in
Italian, it would be necessary to introduce a second
lexicalization set. Only if the lexicalization model is
OntoLex-Lemon, then the lexicalization set shall reference
a lexicon, providing the (reified) lexical entries. A
lexicalization set may include metadata such as the number

1 http://art.uniroma2.it/maple/

5 https://www.w3.org/TR/vocab-dcat-2/

26 https://www.w3.org/2016/05/ontolex/

https://www.dublincore.org/specifications/dublin-core/dcmiterms/
3 http://xmlns.com/foaf/spec/
4 https://www.w3.org/TR/void/

752

https://www.openapis.org/

of lexicalizations, the percentage of the reference dataset
being covered and the average number of lexicalizations
for the entities in the reference dataset.

matching OWL classes vs matching SKOS concepts),
while different combinations of dataset types may requiredifferent matching algorithms or dedicated configurations(e.g. taxonomy is encoded by the property rdfs:subClassOf
in OWL ontologies and by the properties skos:broader andskos:narrower in SKOS thesauri).

Wordnets are represented in OntoLex-Lemon by mapping:i) each synset to an ontolex:LexicalConcept, ii) each word
to an ontolex:LexicalEntry, iii) each sense to an
ontolex:LexicalSense. While similar to the one oflexicalized datasets, the structure of wordnets ischaracterized by the use of lexical concepts and specificproperties to bind these to lexical entries thorough lexical
senses. Therefore, their metadata deserved a dedicatedclass, called lime:ConceptualizationSet, which relates alime:Lexicon (describing the collection of lexical entries)
to an ontolex:ConceptSet (describing the collection oflexical concepts). The description of a conceptualization
set may include the number of lexical concepts, the numberof lexical entries, the average ambiguity and averagesynonymy.

MAPLE doesn't commit on any alignment technique,nonetheless it makes some assumptions:••

The orchestrator finds the lexicalization sets for the input
datasets (see Section 2.1) and produces a ranked list ofpairs of lexicalization sets. The orchestrator also tries toconstruct a synonymizer using a suitable wordnet includedin the metadata registry. The order of the aforementioned
list is determined by a complex scoring formula taking into
account metrics about the lexicalization sets and, if
available, about the synonymizer.

A conceptualization set (and the associated datasets) can beused for synonym expansion; given a word: i) findmatching lexical entries (usually one per POS tag), ii) for
each matched lexical entry, find the associated lexicalconcepts, and iii) retrieve other lexical entries associated toany of these lexical concepts.

2.2

the seeding role of natural language
lexicalizations
the possibility to use wordnets for synonym
expansion (and, in the future, for translation)

The orchestrator will compile a task report with the output
of its analysis, which can be communicated to thealignment system.

MAPLE: MAPping architecture based on
Linguistic Evidences

3.

Use Case and Requirements

As a software framework, MAPLE needs to be integrated
into other systems, which in turn must implement or
consume interfaces defined by MAPLE. Figure 1 illustrates
a concrete use case applying MAPLE to VocBench 38(Stellato et al., 2017; Stellato et al., in press), an opensource web application supporting collaborative editing of
ontologies, thesauri and lexicons, complying with
Semantic Web representation standards.

MAPLE is a framework facilitating the orchestration ofdifferent, loosely coupled actors with the aim to support arobust matching system. A user defines a matching task asa pair of datasets, say 𝐷𝑙𝑒𝑓𝑡 and 𝐷𝑟𝑖𝑔ℎ𝑡 . The purpose of
MAPLE is to facilitate the configuration of a downstreamalignment system to solve this task. To this end, MAPLE
provides an orchestrator that analyzes the input datasets, inorder to infer the characteristics of the task and hint at
promising alignment strategies.

In this use case, the matching task comprises two datasetsthat are managed as two projects in VocBench.

The orchestrator looks up the two datasets in a metadata
registry to retrieve metadata about them: indeed, thedescriptions of the two datasets jointly characterize(perhaps indirectly) the matching task.

VocBench provides an implementation of the metadata
registry that covers locally managed datasets and remoteones (which are not associated with a VocBench project).

MAPLE specifies a metadata application profile that acompliant registry must obey to, while the actual
implementation of the registry is part of the integration withother systems. In this manner, it is possible to adopt andswitch different strategies to acquire and store the metadata(e.g. automatic profiling, manual addition or retrieval of
metadata published alongside the datasets). The
orchestrator and other downstream components in theprocessing chain are completely unaware of the chosen
strategy.

The task report produced by the orchestrator provided by
MAPLE is returned to the user for explanation andrefinement. The (possibly refined) task report is sent to thealignment service for the actual execution of the alignment
task. The need for accepting the task report as an input
instead of obtaining it from the orchestrator is motivated by
the necessity to include the user in the loop.
In addition to the task report, the alignment service may
accept some configuration parameters. The configuration issplit in two: a system configuration that does not depend on
the (explicit) choice of a matcher, and a matcher
configuration that is bound to a specific matcher. Matchersand configuration schemas are clearly dependent on thealignment service, whose interface must include operations
for retrieving them.

The orchestrator uses the metadata about the input
datasets, to determine which information is available, how
it is represented, and the extent of overlap between the twodatasets.
The orchestrator first determines the nature of the input
datasets (i.e. their metamodel), identifying whether they are
ontologies, thesauri and other RDF datasets. Thisknowledge is important to set the goal of the alignment (e.g.

The computation of an alignment can be a slow task;
therefore, it should be handled asynchronously without
blocking the application (and thus the user) who submitted

8 http://vocbench.uniroma2.it/53

Figure 1: Use case integrating MAPLE, VocBench 3 and an external alignment service
it. Additionally, the alignment service shall support thesubmission of multiple tasks.

itself using the OpenAPI format rather than from theimplementation of a reference server. These API
specifications are a first-class artifact of the development
process and, as such, they can be version controlled,
verified, validated and published. Interoperability between
clients and servers is guaranteed by the compliance to thesame specifications. In fact, compliance to a given API isfacilitated by tools that generate client libraries (toconsume the API) and server stubs (to ease theimplementation of the API) from the API definition. One
such tool is Swagger Codegen9, which supports tens ofdifferent programming languages. Moreover, a lot of API
tools can be configured for a certain API by simply loading
its definition in OpenAPI format.

When an alignment task is completed, the user should be
able to download the alignment into an alignment
validation user interface. Validated alignments can then be
integrated into either the left or right dataset. Moreover, theuser can store that alignment into an EDOAL project
dedicated to the alignment between these two datasets.

4.

API Design Methodology

We designed a resource-centric API without using
hypermedia (see Section 5.2), which is required by a strict
compliance to the REST architectural style (Fielding,2008). This kind of API, often called “pragmatic REST”,
comprises a collection of resources associated withendpoints (i.e. URLs) that can be operated on throughstandard HTTP verbs (e.g. GET to retrieve therepresentation of a resource, POST to create a new resource
in a collection, etc.).

5.

We analyzed the use case described in Section 3 to identify
the resources, their representation and the verbs supported
by each of them.
For the development of our API, we adopted the API-first
approach: i.e. we started from the specifications of the API
9

API Definition

The design of a REST API is focused on the identificationof the resources in the domain of interest, theirrepresentations and the HTTP verbs that they support. Eachkind of resource is often associated with two paths (orendpoints): i) the collection of resources of that kind (e.g.
/matchers), ii) each specific resource of that kind (e.g.
/matchers/1). We represented the resources and the request
bodies using JSON, which is currently the de facto standard
for web APIs. In our API, some resources are read-only,
because they reflect the capabilities of a specific alignment

https://swagger.io/tools/swagger-codegen/

54

{

{

"service": "Genoma REST API","version": 1,"status": "active","documentation": "https://../Home","specs": ["http://../alignment-services1.0.0.yaml"],"configuration" : {...
}}"id": "example-matcher","description": "example matcher","configuration": {"type": "object","properties": {"structuralFeatures": {"description": "whether to use
structural features or not","type": "boolean","default": true},
"synonymExpansion": {"description": "whether to dosynonym expansion or not","type": "boolean","default": true}}}}

Listing 1: Representation of the root resource
service or the result of a computation, while others can be
manipulated. This distinction manifests in the support forverbs other then GET.

5.1

Resources

5.1.1
API root
The path / is the root of the API namespace. Performing a
GET on this path returns a JSON object like the one in
Listing 1.

Listing 2: Representation of a matcher
interface to edit the configuration. Instead of reinventing
the wheel, we adopted a subset of JSON Schema10.
If the alignment service does not support manual selection
and configuration of the matcher, this collection should be
empty.

The object contains metadata about the implementation of
the alignment service such as its name (service), version,
compliance to different specs and an optional system
configuration schema (see Section 5.1.2). The specs
property is an array of URLs for locating API definitions in
the OpenAPI format. This array must contain at least the
URL of the description of our REST API. Humans (e.g.
developers) interfacing with this service may benefit froma reference to the documentation of the service.

5.1.3
Tasks
The computation of an alignment is managed as an
asynchronous task, which needs to be modeled explicitly.
The path /tasks is the collection of all tasks ever submitted
to the alignment service. The description of individual tasks
can be obtained from the endpoint /tasks/{id}. Listing 3
contains a JSON object that exemplifies the representationof a task.

When a sever has been just launched, it is not obvious when
it is ready to accept requests. An approach to answer thisquestion is to first attempt to retrieve the representation of
the root: it can be assumed that a sever is not ready as longthe service doesn’t respond at all. Once the representationof the root path is returned, the property status tells whether
the service is starting, active, busy (i.e. no longer accepting
task submissions), shutting down or failed.

The id identifies this task and it can also be found inside thepath associated with the task. The properties leftDataset
and rightDataset reference the two datasets to align. The
service may differentiate between the submission time,
when the task was first queued into the system, and the start
time, when the computation started (pragmatically, when
the service allocated computing resources for the task). An
end time is also included when the execution ends. In fact,
the task status makes it possible to differentiate between atask that is just submitted, running, failed or completed.
When a task is running, its start time is non null and theservice is computing the alignment. The task will

5.1.2
Matchers
The design goal of MAPLE is to disburden the user frommanual configuration of the matching process to the
maximum extent possible. However, an alignment service
may support an option for manually choosing between
different matchers (i.e. often associated with different
combinations of matching techniques).

{

The path /matchers is the collection of all available
matchers, whereas the path /matchers/{id} represents an
individual matcher.

"id": "c27d77380cf4[…]020871d5f95c2","leftDataset":"http://example.org/void.ttl#EuroVoc","rightDataset":"http://example.org/void.ttl#TESEO","submissionTime": "202-0210T18:00:00+01:00","startTime": "202-02-10T18:00:30+01:00","status": "running","progress": 60}

Listing 2 illustrates the JSON object describing a matcher,which contains its identifier (id), a textual description andan optional configuration schema.
The configuration schema defines the “shape” of the JSON
object that represents the actual matcher configuration in atask submission (see Section 5.1.3). Moreover, theconfiguration schema can be used to produce a suitable user
10

Listing 3: Representation of a task

https://json-schema.org/

55

{"taskReport": {"leftDataset": { "@id": "http://example.org/void.ttl#TESEO","conformsTo": "http://www.w3.org/2004/02/skos/core#","uriSpace": "http://www.senato.it/teseo/tes/","sparqlEndpoint": "http://localhost:7200/repositories/TESEO_core"},"rightDataset": { "@id": "http://example.org/void.ttl#EuroVoc","conformsTo": "http://www.w3.org/2004/02/skos/core#","uriSpace": "http://eurovoc.europa.eu/","sparqlEndpoint": "http://localhost:7200/repositories/EuroVoc_core"},"supportDatasets": [{"@id": " http://example.org/void.ttl#TESEO_it_lexset","@type": "http://www.w3.org/ns/lemon/lime#LexicalizationSet""sparqlEndpoint": "http://localhost:7200/repositories/TESEO_core","referenceDataset": "http://example.org/void.ttl#TESEO","lexiconDataset": null,"lexicalizationModel": "http://www.w3.org/2008/05/skos-xl","lexicalizations": 3378, "references": 3378,"avgNumOfLexicalizations": 1, "percentage": 1,"languageTag": "it",}, {"@id": "http://example.org/void.ttl#EuroVoc_it_lexset","@type": "http://www.w3.org/ns/lemon/lime#LexicalizationSet""sparqlEndpoint": "http://localhost:7200/repositories/EuroVoc_core","referenceDataset": " http://localhost:7200/repositories/EuroVoc_core","lexiconDataset": null,"lexicalizationModel": "http://www.w3.org/2008/05/skos-xl","lexicalizations": 18545, "references": 7282,"avgNumOfLexicalizations": 2.546, "percentage": 1,"languageTag": "it",}, {"@id": "http://.../omw/MultiWordNet-it-lexicon","@type": "http://www.w3.org/ns/lemon/lime#Lexicon","sparqlEndpoint": "http://localhost:7200/repositories/OMW_core","languageTag": "it", "lexicalEntries": 43011}, {"@id": "http://.../omw/pwn30-conceptset","@type": "http://www.w3.org/ns/lemon/ontolex#ConceptSet","sparqlEndpoint": "http://localhost:7200/repositories/OMW_core","concepts": 117659}, {"@id": "http://.../void.ttl#MultiWordNet_ConceptualizationSet","@type": "http://www.w3.org/ns/lemon/lime#ConceptualizationSet","sparqlEndpoint": "http://localhost:7200/repositories/OMW_core","lexiconDataset": "http://.../omw/MultiWordNet-it-lexicon","conceptualDataset": "http://.../omw/pwn30-conceptset","conceptualizations": 63133, "concepts": 35001, "lexicalEntries": 43011,"avgSynonymy": 0.537, "avgAmbiguity": 1.468}],"pairings": [{"score": 0.7836831074710862,"source": {"lexicalizationSet": "http://example.org/void.ttl#EuroVoc_it_lexset" },"target": {"lexicalizationSet": "http://example.org/void.ttl#TESEO_it_lexset" },"synonymizer": {"lexicon": "http://example.org/void.ttl#OMW_Lexicon","conceptualizationSet": "http://.../void.ttl#MultiWordNet_ConceptualizationSet"
}
}]
}
}

Listing 4: Representation of a task submission (this example doesn't include neither a matcher nor configurations)
eventually end either by completing the computation of thealignment or by failing for some reason. The reason isexpressed as a JSON object with at least the property
message, which shall contain a textual description of the
failure. For a running task, the property progress contains

the percentage (expressed as an integer between 0 and 100)of the task that has been carried on. A completed task is
associated with an alignment that can be retrieved by meansof a GET on the path /tasks/{id}/alignment. The response is56

formatted according the format11 of the Alignment API(David et al., 2011).

bindings between these words and these concepts (i.e. word
senses).

The submission of a task to the system can be made as a
POST to the collection path /tasks. The body of the request(see Listing 4) represents the submission in terms of a task
report and, optionally, a system configuration, a matcherand (only if a matcher is provided) a matcher
configuration.

At the end of the report, the property pairings contains aranked list of pairs of lexicalizations for each of the input
datasets. Each pairing suggests a different strategy tocompare the input datasets from a lexical viewpoint. Ifavailable (as in the example), the pairing also includes asynonymizer describing a strategy for synonym expansion(see Section 2.1).

Let us describe the content of a submission in reverse order,
starting from the optional parts. If the alignment service
allows that, the parameter matcher can be used to manuallyspecify the matching algorithm (see Section 5.1.2). In thiscase, it is also possible to specify a matcher configuration
as a JSON object, which shall conform to the configuration
schema included in the representation of the matcher.
Independently from the choice of a matcher, the user canalso provide a system configuration as another JSON
object, which shall conform to the configuration schema
included in the representation of the root resource (seeSection 5.1.1).

The response of this HTTP request is the description of thetask just created: using the identifier contained in thisdescription, it is possible to poll the alignment service for
updates on the status of the task.

5.2

Linking

Hypermedia is one of the defining characteristics of the
REST architectural style, which is neglected by"pragmatic" realizations like ours. The principle is that the
representations of the resources should include links toother resources and, in general, make it explicit to theclients the available affordances. The design constraint
HATEOAS (Hypermedia as the Engine of Application
State) requires that any state transition of the applications
should be guided by these links. Without hypermedia, theusage protocol of the API should be encoded in the clients,
and possibly communicated through an out-of-bandmechanism.

The task report is the only mandatory part of a task
submission.
At the beginning of the report, the properties leftDataset
and rightDataset contain the descriptions of the twodatasets to match. The descriptions use properties that are
in most cases eponym for properties defined by widelyadopted metadata vocabularies. The description of a dataset
includes its identifier ("@id") (in the metadata registry),
which is used in the rest of the task report to mention thatdataset. The property uriSpace contains the namespace ofthe dataset (corresponding to void:uriSpace), while theproperty sparqlEndpoint contains the address of a
SPARQL endpoint that provides access to the actual
contentofthedataset(correspondingtovoid:sparqlEndpoint).
The
property
conformsTo(corresponding to dcterms:conformsTo) contains the URI
of a modeling vocabulary that defines the type of thedataset (in the example, both datasets are SKOS thesauri).

OpenAPI 3 (the version we used to define our API)
introduced the notion of links: these are not implemented
using hypermedia in the API responses, but are expressedin the API definitions at the operation level. Simplifying, alink tells how part of the response of one operation can beused as argument for another operation. In other words,
these links allow for describing (part of) the usage protocolof the API.
Within our API definitions, for example, we used links totell that the ID contained in the response of creation
operations can be used as an argument of operations for
retrieving the details of a resource or for deleting it.

The property supportDatasets is an array of JSON objects
describing other potentially useful datasets. Like the ones
above, these descriptions also include further properties
that are bound to specific dataset types (@type).

6.

Implementation Report

The OpenAPI definition of the alignment services API isavailable online13.

In the example in Listing 4, the first two support datasetsare lime:LexicalizationSets that provide SKOS-XL labels
in Italian for each of the input datasets. Indeed, Italian isthe only natural language shared by these datasets, andconsequently it is suggested as the basis for a monolingual
matching scenario. The description of these lexicalization
sets includes several properties borrowed from Lime torepresent metrics.

In Section 3, we gave the overall picture of our use case,integrating VocBench, MAPLE and remote alignment
services. Our REST API meets all functional requirements
elicited in that section; however, the VocBench userinterface is not complete yet:•

The other three datasets define a subset of OpenMultilingual Wordnet12 (Bond & Paik, 2012) for Italian: i)the ontolex:ConceptSet describes the set of lexical concepts(i.e. synsets), ii) the lime:Lexicon describes the set of words
in Italian, iii) the lime:ConceptualizationSet describes the•users can't choose a matcher and specify its
configuration nor can they specify a system
configuration
the task report generated by MAPLE can't beinspected or refined by users

The limitations above are clearly deficiencies of thecomponents using the proposed alignment services API
rather than a problem of the API itself: in fact, the11 http://alignapi.gforge.inria.fr/format.html

13

12 http://compling.hss.ntu.edu.sg/omw/

1.0.0.yaml

57

http://art.uniroma2.it/maple/specs/alignment-services-•capabilities of the API (currently) exceed the ability ofother systems to consume them.
We have already implemented a compliant server for the
ontology matching tool GENOMA (Enea, Pazienza, &Turbati, 2015) using Swagger Codegen. Additionally, weplanned the integration of another matching system called
Naisc14 (McCrae & Buitelaar, 2018).

7.

Unfortunately, removing recursion from the definition of
Schema requires some redundancy in its definition andmoreover, sacrificing the support for arbitrary nestinglevels. We need to collect several more examples ofconfiguration objects (see Section 9) to make an informed
choice about whether the limitations introduced by a nonrecursive definition are acceptable.

Evaluation

The focus of our research effort is to provide concrete
reusable support to alignment systems, separating thevertical discovery and exploration of efficient alignment
techniques from the assessment of the alignment scenario
and consequent fine tuning of these techniques to thesituation. While the former is clearly not our goal – and thus
requires no evaluation, as it mostly depends on the specificconsidered systems complying with our framework – we
conducted an evaluation of the consistency of our approachand implementation in terms of specifications and API
validation. Additionally, we provide a qualitative analysis
based on our experiences in applying the API-first
approach to the development of API-compliant
components.

7.1

7.2

Validation of the API

The verification process described in the previous section
is about whether we "built it right". However, it does nottell anything about whether we "built the right thing". With
this regard, we should point out that we implemented (seeSection 6) the use case described in Section 3, allowing theusers of VocBench to actually interact with externalalignment services using MAPLE. This has increased ourconfidence that the API we have defined is appropriate forits purpose. As pointed out in Section 9, we believe that
onboarding of additional alignment systems should notaffect the overall structure of the API, but mainly allow us
to better understand the representation of configuration
objects.

Verification of the Specifications

We used an online validator15 to verify that our API
definition conformed to the OpenAPI format. A nonconforming API definition might still be quite useful as a
documentation for humans; nonetheless, this verification
step is necessary to ensure that tooling16 based on the
OpenAPI format (e.g. code generators, testing frameworks,etc.) correctly process our API definition.

7.3

Qualitative Evaluation of the API-first
approach

Swagger Codegen supports over 20 different languages forthe generation of server stubs and over 40 different
languages for the generation of client libraries.

The validator confirmed that our API was valid, but it
warned of not better specified circular references. We
analyzed the API definition and, by revalidating a carefullyedited definition, we ascertained that these circularreferences arose in the data model: in particular, in the data
type Schema, which represents a JSON Schema that
describes a system configuration (see Section 5.1.1) or amatcher configuration (see Section 5.1.2). Indeed, Schemais defined recursively: i.e. this data type occurs in its own
definition. Let us consider Listing 2, in which the property
configuration holds a Schema object. This schema models
a JSON object that has the properties structuralFeaturesand synonymExpansion. The value of each property is
described recursively through a "nested" JSON Schema. In
the example, the recursion terminates immediately,
because both properties expect a primitive boolean value.
However, a more complex configuration property might
require several levels of nested JSON objects. Anothersource of recursion is the definition of array properties,
whose items are modeled recursively with Schema objects.

The variety of server stubs simplifies the integration ofmatching systems implemented using different
programming languages. In case of GENOMA (seeSection 6), which is written in Java, we eventually decided
to generate a sever stub utilizing the Spring framework. In
fact, the generated stub provided the complete scaffoldingof the server, leaving us just to provide the implementationof the operations of the API inside pre-generated methods.
The generated code dealt with mapping of URL paths andparameters, clearly facilitating conformance to the API.
With this regard, we should mention the automatic
generation of a domain model from the JSON schemas (inthe API definition) that model parameters and response
bodies. This domain model uses standard Java types (e.g.
Strings) instead of more specific types (e.g. RDF4J's IRI).
This is advantageous since the alignment systems may use
different libraries for the same purpose (e.g. RDF4J 19,
Apache Jena20 or OWLAPI21 as RDF middleware).
Initially, we were concerned about losing ourcustomizations when regenerating the server because ofchanges of the API. However, we discovered that thegenerator produces a Java interface (which should not beedited at all) and a class implementing that interface (where
the developer shall place its code). It is possible toregenerate the sole interface, while the IDE easily identifies

Currently, we are aware of these negative consequences ofcircular references:•

We were reported of problems with the OpenAPI
Generator18. For GENOMA, we used Swagger
Codegen without any issue: since GENOMA does
not support custom configurations yet, there might
be latent problems that we did not discover.

The documentation generated by Swagger UI17
doesn't display recursive data types correctly

14 https://github.com/insight-centre/naisc

18

15

19

https://openapi-generator.tech/https://rdf4j.org/
20
https://jena.apache.org/
21
http://owlcs.github.io/owlapi/

https://apitools.dev/swagger-parser/online/
16
https://openapi.tools/
17
https://swagger.io/tools/swagger-ui/

58necessary changes to the class (e.g. new methods, changed
method signatures, etc.). Currently, VocBench (seeSection 3) is the only consumer of our API. In this case, we
could not use the code generator precisely because of thegenerated domain model, which conflicted with the one
already used for the communication with MAPLE: we
preferred to implement the client manually, while the factthat the server was generated accordingly acted as aconformance check.

8.

with the provisioning of computing resources to thealignment services, which are assumed to be up andrunning on a (remote) machine. Moreover, our approachprescribes that the alignment service is actively aided by its
clients, which submit a task report and, optionally, amatcher and some configuration parameters.
The integrated architecture described in Section 3 is close
to the architecture of GOMMA, a "generic infrastructure
for managing and analyzing life science ontologies andtheir evolution" (Kirsten et al., 2011). With respect to
GOMMA, our whole architecture (including VocBench)covers storing versions of ontologies and mappings, and the
invocation of alignment services. We do not cover diffing
of ontologies (and mappings) and their evolution yet.

Related Work

Shvaiko and Euzenat (2013) analyzed the results of recent
evaluation campaigns for ontology alignment22, concludingthat future growth of the field requires addressing eightchallenges. Our work focuses on four of those:
•

•

•

•

9.

matcher selection combination and tuning: notexplicitly addressed by MAPLE, but the task
report is intended to help the alignment service toadapt and fine tune itself in order to fit thecharacteristics of the given matching scenario;
user involvement: while striving to automate mostof the configuration, our approach also foresees
human intervention on both the task report and theconfiguration of the alignment service, as well as
during the later validation of an alignment;
explanation of matching results: in fact, ourapproach focuses on the visibility into the process
for setting up and configuring the alignment
service for a certain task;
alignment management: infrastructure andsupport: the REST API presented in this paper
and, even more, the overall integration describedin Section 3 deal with the infrastructure
supporting the management aspects, such as
execution of alignment tasks, alignment
validation and storage of links.

Future WorkWe represented the resources defined by our API using
JSON (see Section 5), while the schema of the task
submission (see Section 5.1.3) is informally based on Limeand other metadata vocabularies (i.e. by the use of property
names that match the names of the metadata properties).
We will investigate JSON-LD26 to preserve the use of
JSON, while making that correspondence explicit througha JSON-LD context (referenced by the responses of ourAPI).
By disseminating our API, we hope to on-board furtheralignment services beyond the two mentioned in Section 6.
We believe that these services shouldn’t require(substantial) changes to the operations (i.e. path + HTTP
verb), since these are mainly defined from the viewpoint ofclient systems (i.e. that invoke the alignment service).
Conversely, additional alignment services will help us tobetter understand and improve custom configurations (bothat system level and matcher level), which are specific to analignment service. Firstly, as the diversity of custom
configurations increase, we will test the adequacy of thechosen subset of JSON Schema. Problematic areas include
support for complex property values (e.g. structured values,
polymorphism, etc.) and complex dependencies between
configuration parameters (e.g. mutual exclusivenessbetweenproperties, conditional enablement ofconfiguration properties, etc.). More varied configuration
schemas will secondly give us the opportunity tounderstand if there are recurring patterns that deserve being
part of a (possibly optional) standard configuration.

The Alignment Server, bundled with the Alignment API(David et al., 2011), offers a REST API23 that can becompared to ours. In fact, the API of the Alignment Server
has a wider scope: aiming at managing ontology networks,
the Alignment Server supports computation, validation,
storage and retrieval of alignments. Our API is focused on
supporting the computation of alignments, while the rest iscovered by the overall platform described in Section 3.
SEALS24 (Semantic Evaluation At Large Scale) (Gutiérrez,
García-Castro, & Gómez-Pérez, 2010) and HOBBIT25(Holistic Benchmarking of Big Linked Data) (Röder,Kuchelev, & Ngonga Ngomo, 2019) are two European
projects whose outcome is a sustainable infrastructure forthe execution of evaluation campaigns of semantic
technologies in a scalable, systematic, repeatable andtransparent manner. Consequently, their focus is more on i)
unaided execution of heterogeneous systems against shared
tests cases and ii) storage and comparison of test results.
They also describe procedures to package the systems
under test, and they offer a sophisticated platform to
execute the resulting packages. Conversely, we don't deal

The use case presented in Section 3 includes manual
evaluation of alignments aimed at improving their quality.
We will investigate automatic alignment evaluation(performances, quality etc..) as well, even though insteadof reinventing the wheel it could be interesting to see if we
can integrate existing solutions such as HOBBIT.

10. Conclusions
MAPLE addresses the need for robustness in alignment
systems through a metadata-based approach. In this paper,
we concentrated on the interface that an alignment service
should implement in order to comply with MAPLE and

22 Such as the ones organized by OAEI (Ontology Alignment

25 https://project-hobbit.eu/

Evaluation Initiative) http://oaei.ontologymatching.org/
23 http://alignapi.gforge.inria.fr/rest.html
24 http://www.seals-project.eu/

26 https://json-ld.org/

59benefit from its services. Following the API-first
methodology, we started from the specifications of the API
as a machine-readable artifact using the OpenAPI format.
Then, we implemented the API for the alignment system
GENOMA and planned the same for the system Naisc.

P. Cudré-Mauroux, & A. Zimmermann (Eds.), The
Semantic Web. Latest Advances and New Domains(Lecture Notes in Computer Science) (Vol. 9088, pp.
321-336).
SpringerInternational
Publishing.
doi:10.1007/978-3-319-18818-8_20

11. Acknowledgements

Gutiérrez, M. E., García-Castro, R., & Gómez-Pérez, A. I.
(2010). Executing evaluations over semantic
technologies using the SEALS Platform. Proceedings ofthe International Workshop on Evaluation of Semantic
Technologies (IWEST 2010). Shanghai, China: CEURWS.org. Retrieved from http://ceur-ws.org/Vol666/paper11.pdf

This work has been supported by the PMKI project, underthe 2016.16 action of the ISA2 Programme(https://ec.europa.eu/isa2/). ISA2 is a programme of theEuropean Commission for the modernization of public
administrations in Europe through the eGovernment
solutions.

Kirsten, T., Gross, A., Hartung, M., & Rahm, E. (2011).
GOMMA: a component-based infrastructure formanaging and analyzing life science ontologies and theirevolution. Journal of Biomedical Semantics.
doi:10.1186/2041-1480-2-6

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Proceedings of the 7th Workshop on Linked Data in Linguistics (LDL-2020), pages 61–69
Language Resources and Evaluation Conference (LREC 2020), Marseille, 11–16 May 2020c European Language Resources Association (ELRA), licensed under CC-BY-NC

Using Ontolex-Lemon for Representing and Interlinking Lexicographic
Collections of Bavarian Dialects
Yalemisew Abgaz

Adapt Centre, School of Computing
Dublin City University Ireland
Yalemisew.abgaz@adaptcentre.ie
Abstract
This paper describes the conversion of a lexicographic collection of a non-standard German language dataset (Bavarian Dialects) intoa Linguistic Linked Open Data (LLOD) format within the framework of ExploreAT! Project. The collection is divided into threeparts: 1) conceptual content for unique corpus collection - questionnaire dataset ( DBÖ questionnaires) which contains details of
the questionnaires and associated questions, 2) metadata regarding the collection framework - including collectors and hierarchicalsystem of localisations, and 3) lexical dataset ( DBÖ entries) - both unique data collections as answers to the questions and unique datacollections as excerpts of already published sources. In its current form, the DBÖ entries dataset is available in a TEI/XML format
separately from the questionnaire dataset. This paper presents the mapping of the lexical entries from the TEI/XML into an LLOD
format using the Ontolex-Lemon model. We present the resulting lexicon of Bavarian Dialect and the approach used to interlink thedata collection questionnaires with their corresponding answers (lexical entries). The output complements DBÖ questionnaires dataset,
which is already in an LLOD format, by semantically interlinking the original questions with the answers and vice-versa.
Semantic publishing, Historical data, Linguistic Linked Open Data, exploreAT TEI conversion

1.

Introduction

into TEI/XML formats ( exploreAT TEI) (Schopper et al.,2015; Bowers and Stöckle, 2018). A recent conversion of
the dbo@ema database into an LLOD format is performedon the DBÖ questionnaires including authors, collectors,
places, sources and paper slips using OLDCAN ontology(Abgaz et al., 2018b; Abgaz et al., 2018a) in the frameworkof the project exploreAT!.
Despite several efforts made, so far the conversion did
not include the DBÖ entries. First efforts in dealing with
LLOD were made by Wandl-Vogt and Declerck in 2014 tocreate a model for the conversion of the printed dictionary(Declerck and Wandl-Vogt, 2014). The exploreAT TEI data
efficiently supports the query and retrieval of the lexical entries, offers a well-established data model, yet is still not ina native RDF format and is not compatible with the latest
DBÖ questionnaires dataset. With the recent developmentin publishing linguistic data using widespread lexical models such as Ontolex-Lemon (Cimiano et al., 2020; Cimiano
et al., 2016), several efforts are being made in curating, enriching, interlinking and publishing of the DBÖ data in the
LLOD platform.
The Ontolex model is widely used to represent and publish lexical resources (Declerck, 2018; Tittel et al., 2018;
McCrae et al., 2017; Tiberius and Declerck, 2017; BosqueGil et al., 2015). This paper presents an ongoing effort
in the conversion of the current exploreAT TEI entries into
an LLOD format using the Ontolex-Lemon model and the
OLDCAN ontology to link the entries to the corresponding
questions. The core entities contained in the exploreAT TEI
files are identified and the relevant information is extracted
for representing the lexical entries. Since the DBÖ entries
dataset contains diverse information extracted from the paper slips, only the relevant elements are included in the conversion.
The main contribution of this paper includes:

With the adoption of open access policy, public institutions
that deal with a large collection of language resources haveshown a growing interest in the publication of resources aslinked data using machine-readable lexical models available in the LLOD cloud (Chiarcos et al., 2013). Language resources collected over a long period, with wider
geographic coverage and using traditional data collection
methods are still in the process of transformation to makethe data available in a machine-readable, interlinked andinteroperable format. The process widely involves digitisation of both original data collection methods and the collected data from a physical medium such as paper slips,
cards, recordings, etc. Semantically linking the questionnaires along with the collectors, time, medium, etc., opens
new doors for rich and efficient exploration and reuse tosupport multidimensional analysis and exploration of thedata. This multidimensional analysis uses features such as
the question text, authors, collectors, place, and time in addition to the features of the lexical entries such as forms,
Part Of Speech (POS), grammar, etc.
The Database of Bavarian Dialects in Austria [Datenbank
der Bairischen Mundarten in Österreich] (DBÖ), a digitised
non-standard German language resource (Österreichische
Akademie der Wissenschaften, 2018), is one of the rich linguistic and lexicographic resources collected from 19131998 to document the Bavarian Dialect and rural life inpresent-day Austria, Czech, Slovakia, Hungary and northern Italy. This collection roughly contains 762 questionnaires with a total of 24,382 questions and 3.6 million
paper slips comprising answers to individual questions.
There has been a long process of digitisation of the collection including the conversion of the paper-based information to a digital format initially using an old text processing tool called TUSTEP (Barabas et al., 2010), followedby the subsequent conversion of the data into a relational
database (dbo@ema)(Wandl-Vogt, Eveline, 2010) and then

• the conversion of the DBÖ entries dataset using the61standard Ontolex-Lemon model and the linking of the
DBÖ entries with DBÖ questionnaires dataset which
is used to collect the original data. This semantic interlinking flourishes a bi-directional exploration of thedata: from lexical entries to questions and questionnaires and vice-versa using aspects including topics,
authors, collectors, places, paper slips, etc.

into the <form> element to introduce the detail information contained in each element. An entry contained in<entry> ... </entry> block represents a uniquelexical entry. The <entry> element has <form> representing the different forms of the lexical entry. A lexical
entry could have more than one <form> element identified by its attribute ‘type’. The type of a form could be
one of the following five categories: Hauptlemma (Mainlemma), Lautung (Pronunciation), Lehnwort (Loan word),
Nebenlemma (Other lemmas) and Verweislemma (Additional related form). The form with the Hauptlemma also
has the <orth> element representing the orthography ofthe main lemma. A typical form has one or two <orth>
entries identified by the type attribute. The <orth> could
be original (as it appeared on the original paper slip) or normalised (edited by a professional). An entry further has<gramGrp> representing the grammar group of the entry, <sense> representing the sense of the form, <ref>representing additional data such as archive, source, questionnaire number, etc. Finally, an entry has <usg> element
representing the usage of the lexical entry. The usage typeidentifies how the lexical entry is used and in the majorityof the cases, it is a geographic location.

• the analysis of the data in its current form and the mappings from the exploreAT TEI into LLOD and,• the presentation of the challenges and the lesson
learned while converting the data and publishing theresulting lexicon using the Ontolex-Lemon model.
The remaining sections are organised as follows: Section
2. presents the structure and the content of the current
TEI/XML format. The mapping to Ontolex-Lemon modeland the major design decisions are presented in Section 3.
Section 4. discusses the process of interlinking the original questions with the lexical entries, and Section 5. further
presents a systematic interlinking of concepts, generated byexperts at the questionnaire level, and the lexical entries.
Finally, Section 6. presents some of the data quality issues
that need to be addressed before publishing the dataset tothe public.

2.

Listing 1: A snippet of the exploreAT TEI file

The exploreAT TEI Data<entry xml:id="h385_qdb-d1e386" xml:lang="bar"><form type="hauptlemma"><orth type="orig">(Ober)haus</orth><orth type="normalized">Oberhaus</orth></form><gramGrp><pos>Subst</pos></gramGrp><form type="lautung" n="1"><pron notation="tustep">s -..ow˜An h&#xE2;;us</pron><pron notation="ipa" resp="#JB" change="01">s -..ow˜An h&#xE2;;us</pron><gramGrp><gram>[n,sg+A]</gram></gramGrp></form><sense corresp="this:LT1"><def xml:lang="de">Vorhaus im ersten Stock</def></sense><form type="nebenlemma"><orth type="orig">(Obern)haus</orth><gramGrp><pos>Subst</pos></gramGrp><orth type="normalized">Obernhaus</orth></form><ref type="archiv">
HK 385, h3850131.pir, korr. E.V.
</ref><ref type="quelle">Strobl Flachg. Bauer (1972)</ref><ref type="quelleBearbeitet">{4.5d06} s&#xF6;Flachg.:
Sa. Aufn.BAUER&#xB7; (1972) [GaFb2; chTr]</ref><usg type="geo"><placeName type="orig">Strobl Sa.</placeName><listPlace ref="sigle:4.5d06"><place type="Bundesland"><placeName>Sa.</placeName><idno>4</idno><listPlace>...
</place></listPlace></usg></entry>

The main goal of the collection is to document the Bavarian
dialects in Austria and publish it in the form of a dictionary(WBÖ) and an atlas. The digitisation of the data collection
process and its various supporting materials (DBÖ) offereda knowledge base for a comprehensive, joint approach (dictionary + atlas), prototyped within dbo@ema (Scholz et al.,2008; Wandl-Vogt, 2010) and a cultural, Pan-European exploitation, prototyped within exploreAT!. The data is collected using questionnaires and paper slips distributed via
mails and direct interaction with the respondents. The collection suffered several stages of evolution including thescanning and digitisation of paper slips using TUESTEP
file format (Barabas et al., 2010), conversion to MySQL(Barabas et al., 2010) and TEI/XML formats (Schopper etal., 2015). The current version of the exploreAT TEI data
is TEI version 2 which significantly transformed the original data by reducing redundant data categories (Bowers andStöckle, 2018). The exploreAT TEI files are organised into
folders with the corresponding labels from A-z matching to
the physical drawers. Each file contains several elements
representing lexical entries with unique identifiers.
The structure of the entries is not homogeneous. However,there are common elements shared among the majority ofthe entries. These major elements constitute entry, form, orthography, grammar group, POS, sense, etymology, usage,
place and date. The entries further contain additional elements such as quotes, references, notes, bibliographies, etc.
A snippet of the exploreAT TEI file for an entry (“Oberhaus”) is presented in Listing 1.
Each of the above major elements has distinct XML elements and attributes that describe the content of the elements. For keeping the discussion concise, we started
from the <entry> element and subsequently move deep62

Among these elements, the lexicographers who are working in this project have identified the elements that constitute the core of the lexicon. The following section
presents a detailed discussion on how these core elements
are mapped to Ontolex-Lemon model using R2RML mapping. An intermediate relational database is introduced tofacilitate the conversion and to support compatibility with
the DBÖ questionnaires dataset. There are three user requirements that the conversion process needs to deliver.

Expression or Affix with a single part-of-speech,
morphological pattern, etymology and set of senses.
WBOLexicon is created using ontolex:Lexicon andthe following namespaces are used to be defined throughout
all the listings and examples in this paper. The TURTLE
syntax is used to present the resulting data snippets.
@prefix rdf: <http://www.w3.org/1999/02/22-rdfsyntax-ns#>.
@prefix dc: <http://purl.org/dc/elements/1.1/>.
@prefix dct: <http://purl.org/dc/terms/>.
@prefix foaf: <http://xmlns.com/foaf/0.1/>.
@prefix lexinfo: <http://www.lexinfo.net/ontology/2.0/lexinfo#>.
@prefix lime: <http://www.w3.org/ns/lemon/lime#>.
@prefix oldcan: <https://explorations4u.acdh.oeaw.
ac.at/ontology/oldcan#>.
@prefix ontolex: <http://www.w3.org/ns/lemon/
ontolex#>.
@prefix rdfs: <http://www.w3.org/2000/01/rdf-schema#>.
@prefix rr: <http://www.w3.org/ns/r2rml#>.
@prefix skos: <http://www.w3.org/2004/02/skos#>.
@prefix wbo: <https://exploreat.oeaw.ac.at/
WBOLexicon/>.

• The use of standard, and widely used model for publishing the LLOD data. The final dataset should use
existing models that are standardised and widely usedby the lexicographic community.
• The resulting LLOD shall link the lexical entries
with the questions used to collect the data explicitly.
This will create the bridge between the questionnaire
dataset and the lexical dataset.
• The selected method shall consider future semanticenrichment using resources including DBpedia 1 , KBpedia 2 and BabelNet 3 .

3.1.

To achieve this, the prevalent Ontolex-Lemon model is used
for publishing the lexical data on the LLOD platform. The
OLDCAN ontology is also used to preserve the link between the entries and questions. This aspect is dealt withmore detail in the following sections.

3.

Entries and Forms

Entries of the exploreAT TEI dataset are the core elements of the collection. These entries are mapped
to ontolex:LexicalEntry and are added to the
WBOLexicon using ontolex:entry. This is a relatively simple mapping which defines all the entries as
ontolex:LexicalEntry and lays the foundation for
the rest of the elements. The following R2RML mapping
creates instances of a lexical entry and associates each entry
with the WBOLexicon.

Mapping exploreAT TEI to
Ontolex-Lemon

A series of decisions are made to map the core elementsof the exploreAT TEI data into Ontolex-Lemon representation using an intermediate relational database and R2RML
Mapping. The choice of including an intermediate relational database is to support backward compatibility with
the DBÖ questionnaires dataset, which is previously converted from MySQL database (dbo@ema) and also to interlink the lexical data with the questionnaire dataset which
was also based on a relational data model (Abgaz et al.,2018b; Abgaz et al., 2018a).
The Ontolex-Lemon model provides a rich semantics torepresent linguistic resources by presenting morphological and syntactic properties of lexical entries, which
are the core classes of the model. A lexical entry
is a building block of a lexicon which consists of aset of forms and their associated meanings (Cimiano etal., 2016). The lexical entry is connected to a Lexical Concept via evokes/isEvokedby object property. Lexical entry further relates to Lexical Sense using
sense/isSenseOf object property. The core Ontolex
module is presented in Figure 2 (Cimiano et al., 2016).
A lexical entry represents a unit of analysis of the lexicon that consists of a set of grammatically related forms
and a set of base meanings that are associated with all ofthese forms. Thus, a lexical entry is a Word, Multiword<#LexiconEntryTriplesMap>
a rr:TriplesMap;
rr:logicalTable [ rr:sqlQuery """Select ’WBOLexicon’ as lexicon, e.id, e.lang fromentry e; """ ];rr:subjectMap [rr:template "https://exploreat.oeaw.ac.at/{lexicon}";rr:class ontolex:Lexicon ;rr:graph lexGraph: ;] ;rr:predicateObjectMap [rr:predicate ontolex:language ;rr:objectMap [ rr:column "lang" ] ;rr:graph wbo:lexicon_graph;];rr:predicateObjectMap [rr:predicate ontolex:entry;
rr:objectMap [rr:template "https://exploreat.oeaw.ac.at/
WBOLexicon/LexicalEntry/{id}" ;rr:graph wbo:lexicon_graph;]; ];.

The mapping retrieves all the entries in the database andrepresent them as lexical entries of the lexicon. The resulting lexicon and its lexical entries are presented below.
wbo:WBOLexicon a ontolex:Lexicon ;ontolex:entry<https://exploreat.oeaw.ac.at/WBOLexicon/
LexicalEntry/h385_qdb-d1e2>,<https://exploreat.oeaw.ac.at/WBOLexicon/
LexicalEntry/h385_qdb-d1e108>,<https://exploreat.oeaw.ac.at/WBOLexicon/
LexicalEntry/h385_qdb-d1e129>,...

1

https://wiki.dbpedia.org/
http://kbpedia.org/
3
https://babelnet.io/
263

Figure 1: The exploreAT TEI database schema
exploreAT TEI files. To support maximum interoperability
with the legacy data, we stick to the existing id numbers
following trends from similar conversions (Klimek andBrümmer, 2015) use the written representation of theentries.

3.2.

Form, Canonical Form and Other Forms

A form is a grammatical realisation of a lexical entry (Cimiano et al., 2016). All the entries in the exploreAT TEI
data have at least one form which is represented using
ontolex:Form. The form is linked to the lexical entry using ontolex:lexicalForm object property. We
represent the forms with further details by distinguishing
between canonical form and other forms.
In the Ontolex-Lemon model, there is only one canonical form allowed per entry. However, there are fivedifferent types of forms in the exploreAT TEI dataset.
‘HauptLemma’ is the main lemma which is selected
as a canonicalForm and the other four forms aretreated differently. The so-called “Lautung” does not
represent another form of the entry, but it represents
the pronunciation of the entry in Tuestep and IPA
notation.
Thus, this is automatically excluded but
later used to add pronunciation to the entry. “Nebenlemma” is treated as ontolex:otherForm, however,“Verweislemma” and “Lehnwort”, are not considered important at this stage due to the quality of thedata and the ambiguity of the meaning of the categories. Thus, all the forms with type=‘Hauptlemma’are represented as ontolex:canonicalForm,

Figure 2: The Ontolex-Lemon model.

At this stage, the mapping does not distinguish between a word and Multiword Expression.
However, it represents the entries using the general
LexicalEntry class. This is done because the exploreAT TEI dataset does not distinguish between word,
MultiwordExpression and Affix. Furthermore,
detecting German compound words and Affix from thedataset is complex and beyond the scope of this paper.
Each lexical entry is represented using a unique URI
generated from the unique id number of the entries in the64whereas,
type=‘Nebellemma’ is represented as
ontolex:otherForm. The mapping of the form
and the canonicalForm is given below (Note that the
mapping of the otherForm is also similar except thequery used to extract the rows).

using skos:altlabel which will serve as an alternate
label for the form and enable the representation of thestandard form.
<#FormTriplesMapNormalised>
a rr:TriplesMap;
rr:logicalTable [ rr:sqlQuery """Select o.orth_id, o.type, o.orth, f.form_id fromform f inner join orth o on f.form_id =o.form_id
where o.type<>’orig’; """ ];rr:subjectMap [rr:template "https://exploreat.oeaw.ac.at/
WBOLexicon/Form/{form_id}";
rr:class ontolex:Form ;rr:graph wbo:lexicon_graph ;] ;rr:predicateObjectMap [rr:predicate ontolex:writtenRep ;rr:objectMap [ rr:column "orth" ;rr:language "bar";];rr:graph wbo:lexicon_graph ;];rr:predicateObjectMap [rr:predicate rdfs:label;
rr:predicate skos:preflabel;
rr:objectMap [ rr:column "orth" ;rr:language "bar";];rr:graph wbo:lexicon_graph ;] ;------------------------------------------<#FormTriplesMapPronunciationIPA>
a rr:TriplesMap;
rr:logicalTable [ rr:sqlQuery """SELECT pron,notation, form_id FROM pronunciation
where notation=’ipa’; """ ];rr:subjectMap [rr:template "https://exploreat.oeaw.ac.at/
WBOLexicon/Form/{form_id}" ;rr:class ontolex:Form ;rr:graph wbo:lexicon_graph ;] ;<#LexicalEntrycanonicalFormTriplesMap>
a rr:TriplesMap;
rr:logicalTable [ rr:sqlQuery """select e.id, e.lang, f.entry_id, f.form_id fromentry e left join form f on e.entry_id =f.entry_id where f.type =’hauptlemma’; """ ];rr:subjectMap [rr:template "https://exploreat.oeaw.ac.at/
WBOLexicon/
LexicalEntry/{id}";
rr:class ontolex:LexicalEntry ;rr:graph wbo:lexicon_graph ;] ;rr:predicateObjectMap [rr:predicate ontolex:lexicalForm ;
rr:predicate ontolex:canonicalForm ;rr:objectMap [rr:template "https://exploreat.oeaw.ac.at/
WBOLexicon/Form/{form_id}";rr:graph wbo:lexicon_graph ;] ] ;Based on the above mapping, a given form is represented using at least one ontolex:Form andontolex:canonicalForm.
<https://exploreat.oeaw.ac.at/WBOLexicon/
LexicalEntry/h385_qdb-d1e2>
lexinfo:partOfSpeech lexinfo:noun ;a ontolex:LexicalEntry ;ontolex:lexicalForm <https://exploreat.oeaw.ac.
at/WBOLexicon/Form/1> ;ontolex:canonicalForm <https://exploreat.oeaw.ac.at/WBOLexicon/Form/1> ;<https://exploreat.oeaw.ac.at/WBOLexicon/
LexicalEntry/h385_qdb-d1e108>
lexinfo:partOfSpeech lexinfo:noun ;a ontolex:LexicalEntry ;ontolex:lexicalForm <https://exploreat.oeaw.ac.
at/WBOLexicon/Form/4> .
ontolex:canonicalForm <https://exploreat.oeaw.ac.at/WBOLexicon/Form/4> ;3.3.

rr:predicateObjectMap [rr:predicate ontolex:phoneticRep ;rr:objectMap [ rr:column "pron";rr:language "ipa"; ] ;rr:graph wbo:lexicon_graph ;];3.3.2. Pronunciation
The pronunciation of the “Hauptlemma” is included ina separate form element with type “Lautung”.
All
the variant pronunciations with IPA notation and theso called Tustep notation are also included inside<pron> element with notation attribute.
This information about the pronunciation is represented using
ontolex:phoneticRepresentation. Below, We
demonstrate the result of the mapping of both Tustep and
IPA notations.

Form Written Representation and
Pronunciation

The exploreAT TEI data contains the <orth> element
embedded inside the form element. The <orth> element represents the orthography of the “Hauptlemma” or“NebenLemma”.
3.3.1. Written Representation
The orthography of a lexical entry is represented byontolex:writtenRep. The exploreAT TEI dataset
also uses a type attribute to distinguish between the original written representation and the normalised representation. The normalised representation transforms the original orthography which contains several diacritic marks andspecial characters into a normalised representation. I took
the normalised representation as a written representation of
the form. This is done for two reasons. First, the character
encoding of the original representation is not human readable, and the second, search and retrieval with such representation will pose a difficulty.
The written representation is further enriched byskos:prefLabel and rdfs:label.
The content of the original written representation is also captured<https://exploreat.oeaw.ac.at/WBOLexicon/Form/1>
a ontolex:Form ;ontolex: rdfs:label "Oberhaus"@bar ;skos:altlabel "(Ober)haus"@bar ;skos:preflabel "Oberhaus"@bar ;ontolex:phoneticRep "’s Oberhaus"@ipa,s"’s Oberhaus"@tustep ;ontolex:writtenRep "Oberhaus"@bar .

3.4.

Part of Speech (POS) and Grammatical
GroupsThe POS of an entry which applies to all the forms within
an entry is provided inside the <gramgrp> element. ThisPOS applies to all the forms except those forms which havetheir grammar group. If a form has its grammar group andif the POS is defined there, this form will get its POS instead of inheriting the entry-level POS. Whenever a POS

65

TEI

Lexinfo

TEI

Lexinfo

Gram

Gram

Gram

Gram

Verb
Subst
Pron
Adv
Adj
Interj
Num
Conj
Prep

verb
noun
pronounadverbadjective
interjection
numeralconjunction
fusedPreposition

Verb Verb
Subst Subst
Adj Adj
Adv Adv
Adj Subst
Verb Subst
Subst Prep
Affix

verb
noun
adjective
adverb
?
?
?
?

[P2/1+A][P2/1][P1/1,n+A][P2][P2/1+U][sg+U][sg2+5P2][sg1+5P1]
[sg2]
[D1]

[sg3+5P3][pl1+5P1][imp,sg2][pl3+5P3][kj][P1][kj,sg1+5P1][imp][+5P1][sg1][sg3][pl2+5P2][kj,pl3+5P3]
[+7]
[pl3]
[sg3+5P3][kj,sg3+5P3][kj,sg2+5P2][kj,pl1+5P1][kj,pl2+5P2][D1,n+A][I/1,n+A][P1/1,f+A][P1/1,f][sg3+0]

Table 1: Mapping of POS between exploreAT TEI and Lexinfo.

[m+A]
[m+U]
[il] [m+A][+A]

Table 2: Sample grammar group observed in the dataset

information is identified inside the <form> element, it ismapped to lexinfo:pos in addition to the POS associated with the entry. In the exploreAT TEI, there are 17
different POS used whereas in lexinfo there are only 13(Buitelaar et al., 2011). A partial mapping of the POS
from the exploreAT TEI to lexinfo is implemented during
the mapping process shown in Table 1. There are also POS
instances (with question marks) which are not mapped tolexinfo due to ambiguous POS elements.

attribute @n.
<#SenseTriplesMap>
a rr:TriplesMap;
rr:logicalTable [ rr:sqlQuery """select s.sense_id, sense_definition from Sense sleft join sense_definition sd
on s.sense_id =sd.sense_id; """ ];rr:subjectMap [rr:template "https://exploreat.oeaw.ac.at/
WBOLexicon/Sense/{sense_id}" ;rr:class ontolex:LexicalSense ;
rr:graph wbo:lexicon_graph ;] ;rr:predicateObjectMap [rr:predicate dct:description ;rr:objectMap [ rr:column "sense_definition";
rr:language "de"; ] ;rr:graph wbo:lexicon_graph ;];<https://exploreat.oeaw.ac.at/WBOLexicon/
LexicalEntry/h385_qdb-d1e2>
lexinfo:partOfSpeech lexinfo:noun ;<https://exploreat.oeaw.ac.at/WBOLexicon/
LexicalEntry/h385_qdb-d1e689>
lexinfo:partOfSpeech lexinfo:noun ;<https://exploreat.oeaw.ac.at/WBOLexicon/
LexicalEntry/h385_qdb-d1e72>
lexinfo:partOfSpeech lexinfo:adverb ;At this stage, sense is mapped to ontolex:Sense
and is associated the definition of the sense using
skos:definition and dct:description together
with the language in which the definition is given.

This work looks into the grammar group represented
at the form level. The grammar group identifies between gender, number and case. Here again, an attempt is made to map the grammar groups at the form
level using lexinfo:gender, lexinfo:number andlexinfo:case. However, in the collection, there are
more than one million rows of data related to the grammargroup. What makes it worse is that there are 5,720 unique
combinations of pos, number, gender and case. Supportinga mapping of this grammatical information to the respective representation required significant effort and knowledge. Some of the complexity of the data is shown in thefollowing table where the possible combinations are presented. Due to this complexity, this work does not include
details of the form in the current conversion process andthis task is left for future work (see the lexinfo entries with“?” in Table 1).

<https://exploreat.oeaw.ac.at/WBOLexicon/Sense/1>
dct:description "das obere Stockwerk"@de ;a ontolex:LexicalSense ;skos:definition "das obere Stockwerk"@de .
<https://exploreat.oeaw.ac.at/WBOLexicon/Sense/10>
dct:description "Dachbodenraum; Dachboden"@de ;a ontolex:LexicalSense ;skos:definition "Dachbodenraum; Dachboden"@de .
<https://exploreat.oeaw.ac.at/WBOLexicon/Sense/11>
dct:description "Vorhaus im ersten Stock"@de ;a ontolex:LexicalSense ;skos:definition "Vorhaus im ersten Stock"@de .
<https://exploreat.oeaw.ac.at/WBOLexicon/Sense/12>
dct:description "Husl bei Strengberg"@de ;a ontolex:LexicalSense ;skos:definition "Husl bei Strengberg"@de .

This paper further presents the etymology of the lexical
entries whenever they are available. The etymology ofthe lexical entries represents the origin of the word anda proposed module for representing details of the etymology is presented in (Khan, 2018). Since our etymology collection is not complex, it is represented using thelexinfo:etymology object property linked to the lexical entry. A careful investigation of the etymology datain the collection shows that a further expert analysis of thecontent of the etymological data is crucial for the efficientutilisation by non-expert users.

3.5. Sense, Definition, and Etymology
The entry has ontolex:Sense information which specifies the context in which the given entry is used. The<sense> element also has the <def> element which provides the definition of the word. The sense further contains
the ISO 639-21 language tag which specifies the languageof the definition. Whenever the entry has more than one
sense, additional <sense> element containing the definition is added. These elements are identified using a number

66

object property along with its inverse oldcan:isAnswerOf
property. The Previous example shows the details of aquestion linked with its answers.

https://exploreat.oeaw.ac.at/WBOLexicon/
LexicalEntry/h385_qdb-d1e2>
lexinfo:etymology "s.a. TSA 3,53"@de ;lexinfo:partOfSpeech lexinfo:noun ;a ontolex:LexicalEntry ;ontolex:canonicalForm <https://exploreat.oeaw.ac.at/WBOLexicon/Form/1>;
ontolex:lexicalForm <https://exploreat.oeaw.ac.
at/WBOLexicon/Form/1>;
ontolex:sense <https://exploreat.oeaw.ac.at/
WBOLexicon/Sense/1>.

4.

5.

Interlinking of Questionnaire Concepts to
Lexical Entries

In previous efforts, the questionnaires were linked to DBpedia concepts via a semi-automatic extraction of fine-grainedquestionnaire topics. These topics in combination with thequestionnaire titles were used to extract potential concepts
using DBpedia Spotlight4 . Further, the suggested concepts
with greater than 99% accuracy were evaluated and selectedby subject matter experts. Even if these concepts are a bit
generic, they are very useful in representing the main concepts that are covered by the questionnaires. This gives
us the starting point to link the lexical entries to DBpedia
concepts using ontolex:denotes relationship. At thisstage, an experiment is conducted on some selected questionnaire concepts to see whether it is appropriate to use
these suggested DBpedia concepts for lexical entries. The
result shows that the concepts at the questionnaire level
are too generic and can not be used meaningfully to represent the concepts of the lexical entries. As the assumption is evaluated, the topics in the questionnaires provide
only high-level concepts, whereas the lexical entries provide very detailed concepts. The gap is created because
the concepts in the questionnaires are further specialisedin the questions and subquestions. The lexical entries arecollected in response to the questions and due to this, they
represent very specific concepts. To effectively resolve thisproblem, both bottom-up and top-down approach should be
used. The bottom-up approach seeks to retrieve a matching
concept for the lexical entry from DBpedia and the topdown approach will provide a mechanism to disambiguate
the results of the bottom-up approach. With this idea inmind, this paper demonstrates the potential of the interlinking process to support further enrichment to the collection.
Thus, we decide to relate these questionnaire concepts indirectly via oldcan:isAnswerOf relation (Section 4.),
which link the lexical entries with the questions.

Interlinking Lexical Entries to theOriginal Questions

One of the requirements is to create a meaningful relationship between the different stages of the collection. In(Abgaz et al., 2018b), the data collection method is represented with OLDCAN ontology. The subsequent task
which interlinks the original questions used to collect thedata to the answers is also covered included in the model.
OLDCAN models the answers initially as lemma and subsequently, they are represented as lexical entries using
Ontolex-Lemon. This has not been done initially due tothe absence of information to represent the answers in a detailed form. However, once the exploreAT TEI data is converted into LOD, the next step is to link the questionnairewith the lexical entries.
Each entry in the exploreAT TEI file contains a <ref> element with a pointer to the question number (fragebogenNummer) that combines the questionnaire and the question
number to identify the corresponding question for the lexical entry. This provides crucial information, however, theraw data itself is not represented accurately and it posesa challenge to directly create the required link. To address this problem, the scope is narrowed down to the Systematic, Additional and Dialectographic questionnaires (1120)(Abgaz et al., 2018b) and link the questions of thesequestionnaires with the lexical entries. For the rest of thequestionnaire, currently, it is not possible to resolve thelinks from the data provided in the exploreAT TEI dataset.
<https://exploreat.oeaw.ac.at/WBOLexicon/
LexicalEntry/h385_qdb-d1e108>
lexinfo:partOfSpeech lexinfo:noun ;a ontolex:LexicalEntry ;ontolex:canonicalForm <https://exploreat.oeaw.ac.at/WBOLexicon/Form/4> ;ontolex:lexicalForm <https://exploreat.oeaw.ac.
at/WBOLexicon/Form/4> ;oldcan:isAnswerOf <https://exploreatquestionnaireexplorer.hephaistos.arz.oeaw.
ac.at/Question/13225>.

6.

Data Quality Issues for Further
Improvement

The resulting LLOD data represents the lexicographic collection with rich information using the standard ontolex
model. Sample exploreAT TEI file, the database structure,
the R2RML mapping and some resulting dataset in a TTL
format is available at github5 . Since the final data size islarge, it not available for public use at this stage. The entries are represented using the core classes defined in the
Ontolex-Lemon model. The dataset in its current form,
however, needs further quality checks before it is madeavailable to the public. Some of the data quality issues andpotential remedies are outlined below.

<https://exploreat-questionnaireexplorer.
hephaistos.arz.oeaw.ac.at/Question/13225>
oldcan:isQuestionOf <https://exploreatquestionnaireexplorer.hephaistos.arz.oeaw.
ac.at/Questionnaire/92> ;oldcan:originalQuestion "Wohnhaus/Dachboden:
Dachboden (Speicher, Unterdach, Diele); Ra.
wie: auf der hoh’ Diel’"@de ;a oldcan:SyntacticQuestion;
oldcan:number "F13";
oldcan:shortQuestion "Dachboden (Speicher,
Unterdach, Diele); Fg./Ra.*"@de .

Thus, this work implements the link using theoldcan:hasAnswer object properties with thequestion as a domain and the lexical entry as a range of the4

https://www.dbpedia-spotlight.org/
https://github.com/yalemisewAbgaz/
TEI-XML_Mapping.git
5

67

6.1.

Word, MultiwordExpression and Affix

Expression and Affix and integration of the resulting data
into the visualisation system (Rodrı́guez Dı́az et al., 2019)
developed for the exploreAT! project.

The current conversion of the lexical entries does not use
the subclasses of the ontolex:LexicalEntry. The
entries are not classified as Word, MultiwordExpression or
Affix. In its current form, it is not a trivial task to classify
the lexical entries into the subclasses. However, by combining the grammatical information with external resources
such as GermaNet, BabelNet and DBPedia entries, it is possible to classify the entries with their respective subclasses.
This will improve the quality of the final dataset by incorporating useful details about the entries.

6.2.

Acknowledgements: This research is funded by the Nationalstiftung of the Austrian Academy of Sciences under the funding scheme: Digitales kulturelles Erbe, No.
DH2014/22. as part of the exploreAT! and the ADAPT SFI
Research Centre at Dublin City University. The ADAPTSFI Centre for Digital Media Technology is funded by Science Foundation Ireland through the SFI Research Centres
Programme and is co-funded under the European Regional
Development Fund (ERDF) through Grant # 13/RC/2106.

Part of speech, Grammar and Etymology

The conversion represents a significant portion of the POSof the lexical entries. However, there are some POS entries that are not mapped to lexinfo:partOfSpeech.
There are two options to address this problem. First, involving experts to map the parts of speech that are not mapped
to lexinfo:partOfSpeech and provide the complete
mapping. The second option is to use the parts of speechin the exploreAT TEI files and include them in the OLDCAN ontology to represent them, which is a less preferable
option. The first option will keep the data compatible by using standardised POS used elsewhere, however, it requiresa deeper expert analysis of the cases. This will improve thequality of the resulting LLOD data.
The grammar group is also another area of investigation to deliver a rich lexicon with the grammatical information already available in the exploreAT TEI file. It
requires a deeper analysis of the combinations of thegrammar groups and a method to decipher the grammatical data and map it to the standard grammatical groups,
for example, lexinfo:case, lexinfo:number,lexinfo:gender, etc.
The etymology data and other related data also needs some
improvement. There are several abbreviations, mnemonics
and acronyms that are included in the data. The presenceof such data without the corresponding interpretation will
make the data less usable both by humans and machines.
To address this problem, a scripting language with some expert assistance can be used to transform the abbreviations,mnemonics and acronyms into their corresponding definitions.

7.

8.

Bibliographical References

Abgaz, Y., Dorn, A., Piringer, B., Wandl-Vogt, E., andWay, A. (2018a). A semantic model for traditional
data collection questionnaires enabling cultural analysis. In John P. McCrae, et al., editors, Proceedings of the Eleventh International Conference on Language Resources and Evaluation (LREC 2018), Paris,
France, may. European Language Resources Association(ELRA).
Abgaz, Y., Dorn, A., Piringer, B., Wandl-Vogt, E., and Way,
A. (2018b). Semantic modelling and publishing of traditional data collection questionnaires and answers. Information, 9(12).
Barabas, B., Hareter-Kroiss, C., Hofstetter, B., Mayer,
L., Piringer, B., and Schwaiger, S. (2010). Digitalisierung handschriftlicher Mundartbelege. Herausforderungen einer Datenbank. In Fokus Dialekt.
Analysieren-Dokumentieren-Kommunizieren;
Olms
Verlag, Hildesheim, Germany.
Bosque-Gil, J., Gracia, J., Aguado-de Cea, G., and MontielPonsoda, E. (2015). Applying the ontolex model to a
multilingual terminological resource. In Fabien Gandon,
et al., editors, The Semantic Web: ESWC 2015 Satellite
Events, pages 283–294, Cham. Springer International
Publishing.
Bowers, J. and Stöckle, P. (2018). Tei and bavarian dialect
resources in Austria: updates from the DBÖ and WBÖ.
In Andrew U. Frank, et al., editors, Proceedings of theSecond Workshop on Corpus-Based Research in the Humanities (CRH-2), pages 45–54. Gerastree proceedings.
Buitelaar, P., McCrae, J., and Sintek, M. (2011). Lexinfo:
A declarative model for the lexicon-ontology interface.
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Chiarcos, C., Cimiano, P., Declerck, T., and McCrae, J. P.
(2013). Linguistic linked open data (LLOD). introduction and overview. In Proceedings of the 2nd Workshop
on Linked Data in Linguistics (LDL-2013): Representing
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data, pages i – xi, Pisa, Italy, September. Association for
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Cimiano, P., McCrae, J. P., and Buitelaar, P. (2016). Lexicon model for ontologies: Community report. Technicalreport, W3C Ontology-Lexicon Community Group.
Cimiano, P., Chiarcos, C., McCrae, J. P., and Gracia, J.,

Conclusion

This paper presents the results of ongoing conversion ofa huge lexicographic dataset from exploreAT TEI format
to a LLOD format to digitally publish the RDF version of
the dictionary of the Bavarian dialects. In the conversion
process, the core elements of the exploreAT TEI data aretransformed into Ontolex-Lemon classes and properties.
As the data is not homogeneous, the mapping process is
not always straightforward, however, the implementation
tries to identify the best mappings for each of the selected
data. This is the first stage of the transformation of the
exploreAT TEI data by focusing on the core elementsof the dataset. Future work will include the enrichmentof the LOD data with additional information including
fine-grained DBpedia concepts for each lexical entry,
enrichment of the lexical entries into Word, Multiword

68

(2020). Modelling Lexical Resources as Linked Data,
pages 45–59. Springer International Publishing, Cham.
Declerck, T. and Wandl-Vogt, E. (2014). Cross-linking
Austrian dialectal dictionaries through formalized meanings. In Andrea Abel, et al., editors, Proceedings ofthe XVI EURALEX International Congress. EURAC research, July.
Declerck, T. (2018). Towards a linked lexical data cloud
based on ontolex-lemon. In Proceedings of the EleventhInternational Conference on Language Resources and
Evaluation (LREC 2018), page 7–12, Miyazaki, Japan.
Khan, A. F. (2018). Towards the representation of etymological data on the semantic web. Information, 9(12).
Klimek, B. and Brümmer, M. (2015). Enhancing lexicography with semantic language databases. Kernerman
DICTIONARY News.
McCrae, J., Bosque-Gil, J., Gracia, J., Buitelaar, P., andCimiano, P. (2017). The ontolex-lemon model: Development and applications. In Proceedings of the 5th
Biennial Conference on Electronic Lexicography (eLex
2017), page 587–597, Leiden, The Netherlands.
Rodrı́guez Dı́az, A., Benito-Santos, A., Dorn, A., Abgaz,
Y., Wandl-Vogt, E., and Therón, R. (2019). Intuitive
ontology-based sparql queries for rdf data exploration.
IEEE Access, 7:156272–156286.
Scholz, J., Bartelme, N., Fliedl, G., Hassler, M., Kop, C.,
Mayr, H., Nickel, J., Vöhringer, J., and Wandl-Vogt, E.
(2008). dbo@ema. a system for archiving, handling andmapping of heterogeneous dialect data for dialect dictionaries. In Proceedings of the XIII euralex International
Congress, pages 1467–1472. Documenta Universitaria.
Schopper, D., Bowers, J., and Wandl-Vogt, E. (2015).
dboe@tei: Remodelling a data-base of dialects into arich lod resource. In Proceedings of the 9th International
Conference on Tangible, Embedded, and Embodied Interaction (TEI 2015), Stanford, CA, USA.
Tiberius, C. and Declerck, T. (2017). A lemon model for
the ANW dictionary. In Iztok Kosem, et al., editors, Proceedings of the eLex 2017 conference. Biennial Conference on Electronic Lexicography (eLex-17), Lexicography from scratch, September 19-21, Leiden, Netherlands,
pages 237–251. INT, Trojı́na and Lexical Computing,
Lexical Computing CZ s.r.o., 9.
Tittel, S., Bermúdez-Sabel, H., and Chiarcos, C. (2018).
Using rdfa to link text and dictionary data for medieval french. In Proceedings of the Eleventh International Conference on Language Resources and Evaluation (LREC 2018), page 7–12, Miyazaki, Japan.
Wandl-Vogt, E. (2010). Point and find: the intuitive user
experience in accessing spatially structured dialect dictionaries. Slavia Centralis, pages 35–53, 02.

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[Database of Bavarian Dialects in Austria] (DBÖ).
Österreichische Akademie der Wissenschaften.

Language Resource References

Wandl-Vogt, Eveline. (2010). Datenbank der bairischen
Mundarten in Österreich electronically mapped[Database of the Bavarian Dialects in Austria electronically mapped] (dbo@ema). Österreichische Akademie
der Wissenschaften.
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Proceedings of the 7th Workshop on Linked Data in Linguistics (LDL-2020), pages 70–74
Language Resources and Evaluation Conference (LREC 2020), Marseille, 11–16 May 2020c European Language Resources Association (ELRA), licensed under CC-BY-NC

Involving Lexicographers in the LLOD Cloud with LexO, an Easy-to-use
Editor of Lemon Lexical Resources
Andrea Bellandi, Emiliano Giovannetti
Institute of Computational Linguistics
Via G. Moruzzi, 1 - Pisa, Italy{name.surname}@ilc.cnr.it
Abstract
In this contribution we show LexO, a user-friendly web collaborative editor of lexical resources based on the lemon model. LexO has
been developed in the context of Digital Humanities projects, in which a key point in the design of an editor was the ease of use bylexicographers with no skill in Linked Data or Semantic Web technologies. Though the tool already allows to create a lemon lexicon
from scratch and lets a team of users work on it collaboratively, many developments are possible. The involvement of the LLOD
community appears now crucial both to find new users and application fields where to test it, and, even more importantly, to understandin which way it should evolve.
Keywords: Semantic Web, tools for E-Lexicography, lemon model

1.

Introduction

least to the best of our knowledge) handle the lemon model.
The first one is “lemon source”, a Wiki-like web interface
to manipulate and publish lemon data in a collaborative way(McCrae et al., 2012a). lemon source allows a user to upload a lexicon and share it with others. This tool was developed as an open-source project, based on the lemon API,and it is freely available for use online. However, it can only
manage older versions of the lemon model, and it doesn’t
seem to be updated anymore.
The tool that appears to be the most similar to LexO is
VocBench (Stellato et al., 2017) a web-based, multilingual,collaborative development platform born to manage OWL
ontologies and SKOS(/XL) thesauri and more recently updated to manage also Ontolex-Lemon lexicons and genericRDF datasets. VocBench is a very well engineered tool,
conceived for users with experience in SW technologies
and vocabularies. Indeed, the main difference between
VocBench and LexO lies in target users and the required
expertise to use the tool. As a matter of fact, LexO is aimed
at lexicographers who have no (or very limited) skills in
representing lexica using the OntoLex-Lemon model and,more in general, in using SW and LD related technologies.
However, the ease with which a user can create a lemon
lexical resource in LexO is counterbalanced by the little
flexibility it currently offers, for example to extend the underlying lexical model with custom classes or relations, as
VocBench allows to do.

The increasing growth of the Linguistic Linked Open Data
Cloud1 (LLOD) witnesses the liveliness of activities carriedout by the relative community (in which the Open Linguistics Working Group2 stands out) in the last few years. In
this context, the availability of models and tools can be crucial to attract people (such as linguists, lexicographers, terminologists) willing to be involved but who feel worried by
the lack of standards and, at the same time, by the technological skills required to face the construction of a resource
as a linked data.
A solution to the first issue has come from the definitionof lemon, a model developed to create resources formallydescribing lexicalizations of ontological concepts (McCrae
et al., 2012b). The lemon model has been designed to compensate for the way in which the Web Ontology Language(OWL) (McGuinness and Harmelen, 2004) allows to denote a concept, i.e. by simply labelling it with a string andnot, as it can be desired, with a complex lexical unit. Today,
lemon can be considered a de facto standard for the representation of computational lexicons in the Semantic Web(SW). The number of users potentially interested in editing or consuming lemon data is large (McCrae et al., 2017).
However, the construction of a lexicon based on lemon canbe a tedious process, due to the complexity of the model(constituted by a number of different modules) and on theneed of relying on complex patterns, such as reification, torepresent certain phenomena.
In order to face the second issue, relative to the aforementioned “technical bottleneck”, we have developed LexO3(Bellandi et al., 2019; Bellandi and Khan, 2019), a collaborative, easy to use, web editor of lexical resources basedon the lemon model. In the context of Semantic Web (SW),very few editors of lexicons exist, and just two of them (at2.

An Overview of LexO

LexO4 is a collaborative web editor for easily building andmanaging lemon resources. The features of LexO were
defined on the basis of our experience gained in the creation of lexical and terminological resources in the framework of several projects in the field of Digital Humanities.
The context in which the first version of LexO was devel-1

https://linguistic-lod.org/
http://linguistics.okfn.org
3
It is fully compatible with the latest version of lemon, developed by the Ontology Lexicon (Ontolex) community group.
Please, see https://www.w3.org/2016/05/ontolex/
2

4

The source code is available at https://github.com/
andreabellandi/LexO-lite. You can find a demo at
http://klab.ilc.cnr.it/talmud/LexO70

oped is DiTMAO5 , a project aimed at producing a digitalborn multilingual medico-botanical terminology pivoting
on Old Occitan and developed by philologists (Bellandi
et al., 2018b). Then, a team of lexicographers applied
LexO to create FdS6 , a multilingual diachronic lexiconof Saussurean terminology in the framework of an Italian
PRIN project (Bellandi et al., 2018a). Finally, a bilingual
Chinese-Italian resource was created with LexO on the basis of Matteo Ricci’s Atlas in the context of Totus Mundus7
project (Piccini et al., 2018).
Currently LexO is being used in the context of the Babylonian Talmud Translation Project in Italian8 , to createa multilingual Aramaic/Hebrew/Italian terminological resources of the talmudic domain. The examples that will
follow are based on this work. During the developmentof LexO we have also been influenced by some of thelatest works taking place in the European H2020 project
ELEXIS (in which the Institute of Computational Linguistics of Pisa is involved as a partner). In particular, our work
has been closely informed by the survey of lexicographic
users’ needs conducted as part of the project and recently
published as a deliverable9 . The main aim of LexO is toencourage the involvement of lexicographers in the LLOD
community by i) spreading the use of the lemon model also
in communities of lexicographers new to the modelling ofresources as linked data ii) make the model available to lexicographers already involved in LD and SW but who do nothave the skills to handle the required technologies.
In this sense, LexO acts as a filter between the formal representation language encoding the lexicon and the user, allowing the latter to focus on the lexicographic work without
having to take care of the underlying modeling technology.
To bring an example, to establish a translation relation between two lexical senses the user does not have to know
which properties are to be involved and how to instantiate
the reification pattern required by the “variation and translation” (vartrans) module of lemon. Another key feature of
LexO is its collaborative nature. As a web application, it
allows a team of users with different roles (administrators,
editors, viewers, etc.) to work on the same resource at thesame time. The issue of concurrent access has been faced
by implementing locking mechanisms. As soon as a user
starts editing a certain lexical entry, the system locks it andall the related entities, avoiding the concurrent editing ofother users.
Finally, LexO provides a set of services implemented with
RESTful protocols that give software agents access to theresources managed by the tool. The REST interface implemented in the system allows to query individual resourcesand get information about lexical entries in JSON format.

In the current version, a first set of basic REST services isavailable, though which it is possible to list all the lexicon
languages, the lexicon lemmas, the lemma metadata, thelemmas involved in a specific lexico-semantic relation witha given lemma, and some basic statistics.

Figure 1: The main interface of LexO showing the coremodule.
The system interface has been conceptually designed on thebasis of the lemon model10 . As shown in figure 1, the linguistic information is organized in 3 modules11 :• the core module, to define the structure of each entry,e.g., lemma, other forms, senses;• the variation and translation module, to represent thevariation of relations across entries in the same or different languages (e.g., dialectal, register, and translation relations, or morphological and orthographicones);• the syntactic and semantics module, to describe thesyntactic behavior of an entry, its valence (the syntactic arguments involved by the situation the word refersto) and the link to the ontological structures representing the meaning of the entry.
A particular consideration has to be made regarding thecore module (OntoLex). As already said, LexO is basedon the lemon model, which was designed to lexicalize ontologies in an onomasiological perspective (i.e. from theconcept to the word). LexO, on the contrary, aims at supporting in the construction of lexical resources by startingfrom text, in a more semasiological perspective (i.e. fromthe word to the concept), without requiring that an ontology must be available from the beginning. As a matter offact, the system does not force the user to link each lexical
sense to a concept. However, to stay compliant to the lemon
model, it links an OWL:Nothing class to each new lexical
sense, though in a transparent way to the user. If a user does
want to specify the conceptual reference of a lexical sense,
LexO provides a way to import an OWL ontology and link5

https://www.uni-goettingen.de/en/487498.
html
6Demo available at http://ditmao-dev.ilc.cnr.
it:8082/saussure
7
Demo available at http://lexo-dev.ilc.cnr.it:
8080/TMLexicon
8
www.talmud.it
9
See deliverable D1.1 “Lexicographic practices in Europe: a survey of user needs” at https://elex.is/
deliverables/

10

For describing the properties of linguistic objects, LexO relieson the LexInfo vocabulary.
11
In LexO, the value of linguistic Catalog is automatically
set to LexInfo, an ontology that provides data categories for the
OntoLex-Lemon model.

71

a sense to any of its ontological entities (class, instance orproperty).

2.1.

Working Examples

The main interface of LexO, as shown in Figure 1, is designed for the editing of a multilingual lexicon. It is mainlycomposed of 2 parts. The leftmost column allows a user
to browse lemmas, forms and senses, according to the
OntoLex-Lemon core model. If the resource is multilingual, a lexicographer can filter lemmas, forms and senses
by language. Information related to the selected entry is
shown in the central panel where the system shows the lexical entry of reference, alongside the lemma (red box), its
forms (blue boxes) and the relative lexical senses (yellow
boxes). By means of the plus button it is possible to add
other forms and other senses. Figure 1 shows the entry“presidente del tribunale” (court president) modeled withthe decomp12 module. LexO automatically tries to linkeach multiword component to the correspondent lexical entry, if already available in the lexicon.

Figure 3: The interface to the variation and translation module.
“presidente del tribunale”, and a relation between senses,
i.e. the cultural Italian translation13 with the original sense
associated with the Hebrew term. The encoding generated
by LexO is the following::presidente_del_tribunale_it_entry aontolex:LexicalEntry,
ontolex:MultiwordExpression ;lexinfo:partOfSpeech lexinfo:NounPhrase ;decomp:constituent :presidente_it_comp0,:del_it_comp1, :tribunale_it_comp2 ;ontolex:canonicalForm:presidente_del_tribunale_it_lemma ;ontolex:sense:presidente_del_tribunale_it_sense1 .
:presidente_del_tribunale_it_lemma a ontolex:Form ;ontolex:writtenRep "presidente del tribunale"@it .
:presidente_it_entry lexinfo:head:presidente_del_tribunale_it_entry .
:presidente_del_tribunale_it_sense1 aontolex:LexicalSense ;skos:definition "The Head of the Court ..."@en ;ontolex:reference talmud:courtPresident .

Figure 2: The management of the imported ontology.

:rosh_beit_din_heb_sense1 aontolex:LexicalSense ;As figure 2 depicts, LexO gives users the possibility to import an existing domain ontology, visualize its hierarchicalstructure, its properties and instances, and allows the association of an ontological entity to a lexical sense. In our
example of figure 2, the Talmud domain ontology, which
is being developed within the project, is shown. The senseof the term “presidente del tribunale” is linked to the object
property talmud:courtPresident binding Jewish Rabbis togeographical places.
Through the select button at the top center of the interfaceof figure 1, a lexicographer can switch to the “variation andtranslation” or the “syntax and semantics” part. The former(figure 3) consists of two kinds of relations: i) the semantic
relations holding between senses and including terminological relations (dialectal, register, chronological, discursive,and dimensional variation) and the translation relation; ii)
the relations linking lexical entries and/or forms, which describe, for example, the morphological and orthographic
variations of the word. Figure 3 shows an example of relation between lexical entries, i.e. “presidente” is the head of

Figure 4 shows the syntactic behavior of a word and its
government pattern, namely the actants introduced by theword, their syntactic functions and their morpho-syntactic
realization. These syntactic frames need also to be bound tothe ontological structures representing their meaning. As aconsequence, LexO makes it possible to map the argumentof a predicate defined in an ontology and the syntactic argument introduced in a given syntactic frame.
Figure 4 depicts an example of noun frame of the term having a subject and an (optional) object that has the preposition “di” (of) as marker. An example of frame instancein the Italian translation of the Babylonian Talmud is:“Ma quando giunsero presso il tribunale a Yavnè, rabbànGamlièl, che era il presidente del tribunale, accettò la loro

12
https://www.w3.org/2016/05/ontolex/#decomposition-decomp

13
LexO also uses the vocabulary transcat defined at http://purl.org/net/translation-categories:trans a vartrans:Translation ;vartrans:source:presidente_del_tribunale_it_sense1 ;vartrans:target :rosh_beit_din_heb_sense1 ;vartrans:category transcat:culturalEquivalent .

72

on Linked Data and Semantic Web technologies to be usedby a lexicographer. Though similar tools already exist (inprimis the cited VocBench), we intend to specialize LexOin the ease with which it makes it possible to create lexical
LLOD resources, though at the expense of flexibility andgenerality.
Regarding further developments of LexO, a number of upgrades have already been planned, among which we list thefollowing: i) the integration of a module for the management of text, to allow a user to import a corpus of texts
and use them as a source of lexicographic data; ii) the integration of tools for text processing to support in the construction of the lexicographic resource (for example, given
a lemma, by automatically suggesting candidate forms),
similarly to what Lexonomy (Měchura, 2017) and Sketch
Engine (Kilgarriff et al., 2014) allow to do; iii) the possibility of easily extending the underlying lexical model by
the user, through high level interfaces hiding properties andclasses; iv) the integration of an importing module, to enable a user to upload an existing lexical resource encodedin other models, such as TEI-Lex0 (Bański et al., 2017), or
LMF; v) the enhancement of LexO’s REST APIs for allowing users to get the lexical data in either JSON-LD, LMF,
or TEI-Lex015 ; vi) the possibility to manage the module forlexicography (Bosque-Gil et al., 2017) targeted at the representation of dictionaries16 .
To figure out where to start from and to what extent weshould reassess our planned activities we would like to havefeedback from the LLOD community and from lexicographers willing to create resources in this field. As a matterof fact, it is now crucial both to find new users and application fields where to test it, and, even more importantly, tounderstand in which way LexO should evolve.
Finally, we are planning to perform an evaluation of LexO
focused on its usability by involving some potential users
coming from the LLD community.

Figure 4: The interface to the syntax and semantics module.
testimonianza” (“But when they arrived at the court inYavnè, rabbàn Gamlièl, who was the president of the court,
accepted their testimony”). Basically, the term verbalizes
the property talmud:courtPresident where the subject corresponds to the first argument of the property (i.e., the property domain class), and the object corresponds to the second
argument of the property (i.e., the property range class).
The encoding generated by LexO is the following::presidente_del_tribunale_it_entry aontolex:LexicalEntry,
ontolex:MultiwordExpression ;synsem:synBehavior:presidente_del_tribunale_it_frame1 .
:presidente_del_tribunale_it_frame1 asynsem:SyntacticFrame, lexinfo:NounFrame ;lexinfo:subject:presidente_del_tribunale_it_frame1_arg1 ;lexinfo:object:presidente_del_tribunale_it_frame1_arg2 .
:presidente_del_tribunale_it_frame1_arg1 a
synsem:SyntacticArgument .

4.

Acknowledgements

This work was supported by the TALMUD project and carried out within the scientific collaboration between S.c.a r.l.
PTTB and ILC-CNR (09/11/2017).

:presidente_del_tribunale_it_frame1_arg2 a
synsem:SyntacticArgument ;synsem:marker "of" ;synsem:optional "true"ˆˆxsd:boolean .

5.

:presidente_del_tribunale_it_sense1 a
synsem:OntoMap, ontolex:LexicalSense ;
synsem:ontoMapping:presidente_del_tribunale_it_sense1 ;ontolex:reference talmud:courtPresident ;synsem:subjOfProp:presidente_del_tribunale_it_frame1_arg2 ;synsem:objOfProp:presidente_del_tribunale_it_frame1_arg1 .

Bibliographical ReferencesBański, P., Bowers, J., and Erjavec, T. (2017). Tei-lex0
guidelines for the encoding of dictionary information on
written and spoken forms. In Proceedings of Electronic
Lexicography in the 21st Century, pages 485–494.
Bellandi, A. and Khan, A. F. (2019). Lexicography andthe semantic web: A demo with lexo. In Proceedings ofElectronic Lexicography in the 21st Century, pages 53–
56.
Bellandi, A., Giovannetti, E., , and Piccini, S. (2018a).
Collaborative editing of lexical and termino-ontologicalresources: a quick introduction to lexo. In XVIII EURALEX International Congress Lexicography in Global

Finally, by selecting the “dictionary view” tab, a dictionarylike rendering of all the information related to the selectedentry is shown in the central panel of LexO. The renderinglayout can be modified by replacing the relative CSS files14 .

3. Next StepsIn this work we have introduced LexO, a web collaborative
tool for the construction of lexicographic resources basedon the lemon model. LexO does not require any knowledge

15

Considerations and results coming from ELEXIS projectwill take into account. See for example https://elex.
is/wp-content/uploads/2019/02/ELEXIS_D2_1_
Interface_for_Interoperable_Resources.pdf
16
https://www.w3.org/2019/09/lexicog/

14This operation requires the skills for editing the Java project,substituting the CSS files, and recompiling the LexO project.

73

Contexts – Book of Abstracts, pages 23–27, Ljubljana,
Slovenia.
Bellandi, A., Giovannetti, E., , and Weingart, A. (2018b).
Multilingual and multiword phenomena in a lemon old
occitan medico-botanical lexicon. Information, 9(3) 52.
Bellandi, A., Khan, A. F., and Monachini, M. (2019).
Enhancing lexicography by means of the linked data
paradigm: Lexo for clarin. In Kirl Simov et al., editors,
Proceedings of the CLARIN Annual Conference 2019,
pages 134–137, Leipzig.
Bosque-Gil, J., Gracia, J., and Montiel-Ponsoda, E. (2017).
Towards a module for lexicography in ontolex. In Proceedings of the LDK 2017 Workshops: 1st Workshop on
the OntoLex Model, pages 74–84.
Kilgarriff, A., Baisa, V., Bušta, J., Jakubı́ček, M., Kovář,V., Michelfeit, J., Rychlý, P., and Suchomel, V. (2014).
The sketch engine: Ten years on. Lexicography, 1(1).
McCrae, J., Montiel-Ponsoda, E., and Cimiano, P. (2012a).
Collaborative semantic editing of linked data lexica. In
In the Proceedings of the Language Resources and Evaluation, pages 2619–2625, Instanbul.
McCrae, J. P., Aguado de Cea, G., Buitelaar, P., Cimiano,P., Declerck, T., Gómez-Pérez, A., Gracia, J., Hollink,L., Montiel-Ponsoda, E., Spohr, D., and Wunner, T.,(2012b). Interchanging Lexical Resources on the Semantic Web, pages 701–709. Language Resources and Evaluation. Springer Netherlands.
McCrae, J. P., Bosque-Gil, J., Gracia, J., Buitelaar, P., andCimiano, P. (2017). The ontolex-lemon model: Development and applications. In Proceedings of Electronic
Lexicography in the 21st Century, pages 19–21.
McGuinness, D. L. and Harmelen, F. V. (2004). Owl web
ontology language overview. W3C recommendation 10.
Měchura, M. (2017). Introducing lexonomy: an opensource dictionary writing and publishing system. In Proceedings of Electronic Lexicography in the 21st Century,
pages 19–21.
Piccini, S., Bellandi, A., and Giovannetti, E. (2018).
A semantic web approach to modelling and building
a bilingual chinese-italian termino-ontological resource.
In XVIII EURALEX International Congress Lexicography in Global Contexts – Book of Abstracts, pages 87–
90, Ljubljana, Slovenia.
Stellato, A., Turbati, A., Fiorelli, M., Lorenzetti, T.,
Costetchi, E., Laaboudi, C., Gemert, W. V., and Keizer,
J. T. (2017). Vocbench 3: Pushing collaborative development of thesauri and ontologies further beyond. In
Proceedings of 17th European Networked Knowledge
Organization Systems Workshop, pages 39–52.

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Proceedings of the 7th Workshop on Linked Data in Linguistics (LDL-2020), pages 75–81
Language Resources and Evaluation Conference (LREC 2020), Marseille, 11–16 May 2020c European Language Resources Association (ELRA), licensed under CC-BY-NC

Supervised Hypernymy Detection in Spanish through Order Embeddings
Gun Woo Lee, Mathias Etcheverry, Daniel Fernández Sánchez, Dina Wonsever
InCo, Fing, Universidad de la República
Montevideo, Uruguay{gun.woo.lee, mathiase, daniel.fernandez.sanchez, wonsever}@fing.edu.uy
Abstract
This paper addresses the task of supervised hypernymy detection in Spanish through an order embedding and using pretrained word
vectors as input. Although the task has been widely addressed in English, there is not much work in Spanish, and according to ourknowledge there is not any available dataset for supervised hypernymy detection in Spanish. We built a supervised hypernymy dataset
for Spanish using WordNet and corpus statistics, with different versions according to the lexical intersection between its partitions:random and lexical split. We show the results of using the resulting dataset within an order embedding consuming pretrained word
vectors as input. We show the ability of pretrained word vectors to transfer learning to unseen lexical units according to the results inthe lexical split dataset. To finish, we study the results of giving additional information in training time, such as, co-hyponymy links andinstances extracted through lexico-syntactic patterns.
Keywords: Hypernymy Detection in Spanish, Order Embedding, Word Embedding

1.

Introduction

Hierarchical organizations are key in language semantics.
Hypernymy refers to the general-specific relationship between two lexical terms. Such is the case of biology
taxonomies (e.g. mammal-vertebrate, pangolin-mammal),seasons (e.g. spring-season) and colors (e.g. green-color),
among many others. The general term is called the hypernym and the specific one the hyponym. In natural language
processing, automatic hypernymy detection (or taxonomy
learning) is an active NLP research area, that has applications in several tasks such as question answering (Clark etal., 2007), textual entailment (Chen et al., 2017) and image
detection (Marszalek and Schmid, 2007).
A well known hand-crafted resource is WordNet (Miller,
1995). It is a large lexical database that contains semantic relations, including hypernymy among them. Manual
resources consume a considerable human effort for its creation and maintenance, and suffer from incompleteness andinadequacies. Furthermore, different applications require
the expansion of the hypernymy relationship to particularinstances like celebrities, song names, movies, and so on.
Hence, it is clear the importance of automatic mechanisms
to overcome or assist manual ones.
Regarding Spanish, the resources available for supervised
hypernymy detection are quite scarce. WordNet was originally created for English and later translated into other languages, among which is Spanish (Atserias et al., 2004).
This consists in the main source of hypernyms for Spanish.
Hypernymy detection has been evaluated mainly throughbinary classification relying on datasets that contain a number of pairs of terms and a label for each pair indicating if
hypernymy relation is held between the terms (Shwartz etal., 2016).
A complementary evaluation benchmark for modeling hypernymy is given by hypernymy discovery (Espinosa-Ankeet al., 2016). It consists on given a domain’s vocabulary andan input term, discover its hypernyms. This formulation
is beneficial to avoid the lexical memorization phenomena (Levy et al., 2015). Regarding to hypernymy discovery,

Figure 1: Example of a very simplified taxonomy in Spanish.
a dataset in Spanish (among other languages) was introduced for the task 9 of SemEval-2018 (Camacho-Collados
et al., 2018).
In this work we does not pursuit hypernymy discovery and
we are aware that it is not clear how realistic hypernymy
detection is, since in many scenarios the potential pairs
may not be given and need to be discovered. However, we
believe that a dataset for hypernymy detection in Spanish
can be useful for model comparisons, and according to ourknowledge there is no such resource available for Spanish
at the time of this work.
We introduce a dataset for supervised hypernymy detection for Spanish built using Spanish WordNet and corpus
statistics. We describe its creation process and we madeit available to the NLP community as a complementarybenchmark for hypernymy detection in Spanish. In addition, we train and evaluate using the created dataset an order embedding (Vendrov et al., 2015) based model using
pretrained word embeddings as input, and we report the obtained results for future comparisons. Also, we show that
this model, disregarding the use of Hearst patterns, outperforms other distributional approaches and the much more
complex hybrid LSTM-based model, that combines distributional and path-based information, proposed by Shwartz
et al. (2016).

75

2.

Related Worksstraints. The direction of the asymmetric relation was encoded in the resulting vector norms while cosine distancejointly enforces synonyms semantic similarity. The resulting vectors were specialized simultaneously for lexical relatedness and entailment.

Hypernymy detection in NLP can be focused as a supervised or an unsupervised learning task. Supervised approaches relies on pairs annotated with the information ofwhether they belong to the relationship or not. On thecontrary, unsupervised approaches do not use annotated instances, they rely solely in the distributional inclusion hypothesis (Zhitomirsky-Geffet and Dagan, 2005) or entropy
based measures (Santus et al., 2014).
Supervised approaches have been addressed mainly using
two types of information: paths and contexts distributions(or word embeddings). Path-based (or pattern-based) approaches use the paths of words that connect instances of
the relationship. Hearst (1992) presents the first path-based
approach where hand-crafted patterns were used for hypernymy extraction. For example, the path “is a type of”
would match cases like “tuna is a type of fish” allowing
to detect that “tuna” is an hyponym of “fish”, etc. Also,
paths of joint occurrences in syntactic dependency trees result useful for hypernymy detection (Snow et al., 2004).
Path patterns were generalized using part-of-speech tags
and ontology types by Nakashole et al. (2012). A different kind of pattern-based approach is proposed in the workof Navigli and Velardi (2010), they consider word lattices
to extract definitional sentences in texts and then extract
hypernymy related pairs from them, or learning lexical taxonomies (Navigli et al., 2011). The main disadvantage ofpath-based approaches is that both candidates must occur
simultaneously in the same context.
In the other hand, the distributional approaches relies in thecontexts of each word independently. Many methods propose supervised classification after applying a binary vector operation on the pair of representations, such as vector
concatenation (Baroni et al., 2012) and difference (Roller
et al., 2014; Fu et al., 2014; Weeds et al., 2014). Vylomova
et al. (2016) studied vector difference behavior in a wider
set of lexical relations and they remarked the importanceof negative training data to improve the results. Ustalov
et al. (2017) performed hypernyms extraction based on
projection learning. Instead of classifying the pair of representations, they learned a mapping to project hyponyms
embeddings to their respective hypernyms, remarking also
the importance of negative sampling. A related approachis presented by Dash et al. (2019), where a neural networkarchitecture is designed to enforce asymmetry and transitivity through non-linearities and residual connection. These
last two approaches present some overlap with the work of
Vendrov et al. (2015), that its order embedding approach is
the one considered in this work.
Shwartz et al. (2016) combined path-based and distributional information in supervised hypernymy detection,
concatenating the embedding of each term independently
with a distributional representation of all paths between theterms in a dependency parsed corpus. The representation
was built with the average of the LSTM resulting representation of each path. Additionally, they introduced a dataset
for lexical entailment where they tested their model.
LEAR (Lexical Entailment Attract-Repel) (Vulic and Mrksic, 2017) gives great performance on hypernymy detection specializing word embeddings based on WordNet con-3.

Hypernymy Dataset for Spanish

In this section we describe the dataset construction process.
The dataset consists of pairs of words and a boolean label
associated to each pair that is true when the first element
is an hyponym of the second and false otherwise. We willrefer as positive instances to those pairs that are labelled
as true (e.g. summer-season) and as negative instances tothose that are labelled as false (e.g. cat-fish).
In the dataset construction process we use a variety ofsources to obtain positive and negative instances. In thefollowing we describe each source and technique used; andwe give a measure of the quality of the dataset based on arandom sampling.
In addition and based on the dataset built by Shwartz et al.
(2016), we performed a random split (in train, validationand test) and a split without terms occurring in more than
one partition to deal with the lexical memorization (Levy etal., 2015). The latter is referred as lexical split.

3.1.

Related Pairs

The extraction of positive pairs was performed using Spanish WordNet, patterns against a Spanish Corpus, andShwartz dataset translation.
In addition to these sources, it is possible to consider thetransitive links as positive instances, since the hypernym
relation fulfills the transitive property. However, this assumption may not be satisfied when different senses are
faced in the transitive link. So, we decided to not consider
inferred transitive instances in this work, and the dataset
discard word sense information.
In the following we describe how we use each source:• Spanish WordNet:
The main source of positive instances of our dataset isthe Spanish version of the WordNet of the Open Multilingual Wordnet (OMW). We consider the hypernymy
relation defined in WordNet between synsets, and thenwe perform a selection of pairs, taking one word ofeach synset, to obtain hypernymic pairs that will belong to the dataset.
We considered the following two heuristics:
1. We choose from each synset those words that are
most frequently used according to its frequency
in the corpus of Cardellino (2016)1 .
2. Based on Santus et al. (2014) work, we filtered
the resulting candidate pairs that the hyponyms
has a frequency greater than the frequency of it
proposed hypernym.
1

Spanish Billion Word Corpus and Embeddings by Cristian Cardellino: https://crscardellino.github.io/
SBWCE/

76

k
1
2
3

Size (# pairs)
15695 / 10103
29180 / 19258
35103 / 22851% Correct
83.9 / 84.3
82.2 / 83.3
77.6 / 83.5

dataset contains a considerable number of instances
like shakespeare-writer. Therefore, we consider to select those pairs that contain proper names as hyponym
candidate. We limit our selection to the instances of:“village”, “city”, “company”, “town”, “place”, “river”
and “person”; and we translate the instances through
Google’s translation library. We include the resulting
candidates as positive instances in our dataset.

Table 1: Size and percentage of correct hypernyms of asample of the resulting pairs considering 1, 2 and 3 mostfrequent words of each synset. We show the results applying (right) and without applying (left) the second heuristic
filtering.

3.2.

Unrelated Pairs

The unrelated pairs, or negative instances, are those pairsthat does not hold an hypernymic relation between them.
We consider for the procurement of unrelated pairs the following approaches:

Regarding the first heuristic, we observe the result ofconsidering the pairs from an all-vs-all of the k mostfrequent lemmas of each synset. In table 1 we report
the respective sizes and percentage of correct pairs of
a 0.5% random sample, where can be observed thattaking into account more than the two most frequentwords of each synset the results degrade considerably.

• Random sampling:
Since most of the words are not hypernym between
them, we can randomly pick two words from a given
vocabulary and we probably will get a non hypernymic pair. So, we obtain the noun words from the
Cardellino’s Corpus, with at least 4 characters anda frequency greater than to 200, jointly with the vocabulary of the positive part, above mentioned, of thedataset. Then we proceed to generate tuples, that were
not already included in the dataset, till complete thedesired ratio of 1:3 of positive:negative instances.

We filter the output of the first heuristic using the second heuristic and we observe a quality improvementin the resulting pairs. The values on the right in table 1 details the obtained results. According to thisminimal evaluation criterion we decide to consider themost three frequent words of each synset filtering thepairs where the hyponym is more frequent than the hypernym.

The dataset resulting of WordNet, Shwartz translation
and random pairs is what we refer as our base dataset,presented in its two versions: random and lexical split,
as we will detail later.

To finish with WordNet extracted hypernyms, we
eliminate the cycles that are generated due to the multiple senses of certain words and the transitivity of thehypernym relation. The resulting pairs are the final setof the WordNet positve instances of the dataset.

• Cohyponyms:
Cohyponymy is the relation between hyponyms thatshare the same hypernym. They are words that haveproperties in common, but which in turn have theirown characteristics that differentiate them well fromeach other. Cohyponymy can be seen as words belonging to a same class (e.g male-female, marchnovember). Given a pair of cohponyms it is highly
probably that an hypernymy relation is not fullfilled
between them. Therefore, it is possible to obtain negative pairs from cohyponymic relations entailed fromthe positive instances.

• Pattern-based:
Relying on the well known importance of the pattern(or path) based approaches to detect and discover hypernyms, originated by Hearst (1992), we consider
to include in our dataset positive instances extractedusing high confidence patterns. We consider the following two patterns for Spanish built by Ortega et al.
(2011) they found to present a high confidence in theirexperiments (confidence value near to 1):
1. “el <hyponym> es el único <hyperonym>”
2. “de <hyponym> y otras <hyperonym>”• Inverted links:
The hypernym relation is asymmetric. Therefore, if atuple satisfies the hypernym relation, its inverse not.
Then, having our positive dataset already, a simple
way to build negative dataset is exchanging the order of the pairs of the positive dataset. However, synonyms may become a problem in this assumption. We
can think between some synonyms that an hypernymic
relation is fulfilled in both directions (e.g. neat-tidy).
For this reason we does not include inverted links inthe distributed dataset.

We use these patterns to extract candidate pairs fromthe corpus of Cardellino (2016). Unfortunately, thequality of the resulting pairs was poor. Subsequently,
we achieve a little improvement filtering the obtained
candidates using the part of speech. Even so, we did
not obtain good enough results to be included in the
final dataset. However, we consider that despite thepoor quality the extracted instances, it may become
useful to study the behavior of including them as training data. For that purpose it is available along with thedataset.

• Antonymy:
Words that have an opposite meaning are called
Antonyms. We assume that if there is an antonymy
relationship, the hypernym relationship is not satisfied. Therefore, we include the antonyms extracted
from WordNet as negative instances.

• Shwartz dataset translation:
In the dataset built by Shwartz et al. (2016), theyobtained the hypernymy relation instances from English WordNet, DBPedia, Wikidata and Yago. Their77

WordNet
27861

Positive Pairs
Pattern-based
2731

Random∼ 90000

Negative Pairs
Cohyponym Antonym∼ 450001107

to a non negative vector with a partial order relation defined
and trained to take hypernym pairs to related vectors.
In this work we show that without path or any additional
information than the proper word embedding of each word,
and a feed forward network trained as above mentioned,
fairly good results can be achieved.
We first give an introduction to the order embedding proposal and our experiments configuration.

Shwartz
3798
Meronym
5940

Table 2: Total amount of positive and negative instancesfrom where each version of the dataset is built.

3.3.

4.1.

An order embedding is a function between two partiallyordered sets f : (X, X ) → (Y, Y ) that preserves andreflects its order relationships. That is to say, x1 X x2 if
and only if f (x1 ) Y f (x2 ).
Vendrov et al. (2015) introduce a method to train an order embedding into <m≥0 considering the reversed productorder, defined as follows:

Dataset Splits

As usual in supervised training, we split the whole dataset(positive and negative pairs) into train, validation and test
partitions. Following the work of Shwartz et al. (2016), we
consider two splits of the data: random and lexical split.
While the random split is performed randomly, the lexical
split does not allow lexical intersection between the partitions. In the following section we describe each one.

x  y ⇐⇒

3.3.1. Random Split
The random split consists in splitting the dataset randomly,
without taking into account any consideration. We performa random split with the following ratio: 70 % for training
set, 25 % for test set and 5 % for validation set.
This splitting process has the advantage that any tuple isdiscarded, leading to a larger dataset, but may suffer of thephenomena of lexical memorization (Levy et al., 2015).
The lexical memorization phenomenon occurs when different pairs of hypernym, instead of learning the semantic relationship between words, learn a specific word independently as a strong indicator of the label. For example,
given the positive pairs such as: (cat, animal), (dog, animal), (horse, animal), the algorithm tends to learn that theword “animal” is a “prototype” and given any new (x, animal) classifies it as a positive pair.

P
N
P
N

Train
18654
55962
8221
24663

Val
1332
3996
513
1539

Test
6662
19986
2506
7518

i=1

xi ≥ yi ,(1)

4.1.1. Contrastive Loss Function
The partial order relation (, <m≥0 ) defined above allows
to define measures to quantify the degree to which a pairof two elements does not satisfy the relationship. Let us
considerEp (~x, ~y ) = ||max(~0, ~y − ~x)||2 ,(2)where ~x, ~y ∈ <m
+ and max is the maximum function
element-wise. Note that Ep indicates the relation satisfaction degree and Ep (x, y) = 0 iff ~x  ~y .
Then, Ep can be forced to be higher than a threshold α forunrelated terms through the max-margin loss as follows:
En (~x, ~y ) = max{0, α − Ep (~x, ~y )},(3)

guaranteeing that En (x~0 , y~0 ) is 0 when Ep (x~0 , y~0 ) ≥ α andtherefor x~0 y~0 .
Then, summing (2) and (3) the resulting contrastive loss
function, which consists of minimizing Ep and En jointly,
stands as follows:X
X
L=
Ep (~x, ~y ) +En (x~0 , y~0 ),
(4)

Total
10659244960(x,y)∈P(x0 ,y 0 )∈N

where P and N are sets of positive and negative examples,
respectively. Note that L is differentiable allowing to fit a
mapping to an order embedding through gradient descentbased techniques.

Table 3: Spanish dataset sizes for each split: lexical andrandom. The sizes are discriminated in terms of positive(P) and negative (N) instance. This sizes does not containcohyponyms or pattern extracted positive instances.

4.

m
^

where x, y ∈ <m
≥0 and xi and yi correspond to the i-thcomponent of x and y, respectively. By definition this relationship is antisymmetric and transitive, being ~0 the top
element of the hierarchy.

3.3.2. Lexical Split
To avoid the phenomenon of lexical memorization, thetraining, validation, and test sets are split with different vocabularies. We split the dataset with the same methodologyof (Shwartz et al., 2016). The approximate division ratio
was 70-25-5. The respective sizes of the random and lexical
splits of our base dataset are shown in Table 3.
Rnd.
Split
Lex.
Split

Order Embedding Model

4.2.

Experiments using Order Embeddings

Hyperparameter Configuration

We search for a good hyperparameter configuration throughrandom search. We search for an hyperparameter configuration according to the validation set and report the evaluation results on the test set partition. We consider feed

To automatically detect hypernymy we consider a simple
feed forward network trained as an order embeddings (Vendrov et al., 2015). This network takes the word embedding78

OrdEmb(a) OrdEmb +cohyp
OrdEmb +pattern
OrdEmb +pattern +cohyp

Prand
0.855
0.857
0.860
0.859

Rrand
0.904
0.932
0.885
0.930

Frand
0.879
0.893
0.872
0.893

Plex
0.823
0.809
0.798
0.802

Rlex
0.674
0.827
0.766
0.821

Flex
0.741
0.818
0.782
0.811

OrdEmb(b) OrdEmb +cohyp
OrdEmb +pattern
OrdEmb +pattern +cohyp

Prand
0.719
0.847
0.742
0.848

Rrand
0.946
0.869
0.931
0.870

Frand
0.817
0.858
0.826
0.859

Plex
0.744
0.781
0.666
0.759

Rlex
0.841
0.716
0.857
0.678

Flex
0.789
0.747
0.749
0.716

Table 4: Results on test set on Spanish. The upper table (a) shows the result of evaluating without introducing inferredcohyponymy instances in the test partition and the lower table (b) shows the results including cohyponymy instances in thetest partition. The labels +cohyp and +pattern stand for cohyponymy and pattern-extracted instances in the training data.

Best Distributional (Shwartz et al., 2016)
HypeNET Integrated (Shwartz et al., 2016)
OrdEmb ReLU
OrdEmb SELU-ReLU
OrdEmb tanh-sigm

Prand
0.901
0.913
0.936
0.932
0.967

Rrand
0.637
0.890
0.876
0.845
0.836

Frand
0.746
0.901
0.905
0.887
0.897

Plex
0.754
0.809
0.958
0.740
0.788

Rlex
0.551
0.617
0.615
0.872
0.756

Flex
0.637
0.700
0.749
0.801
0.771

Table 5: Order embedding results with different activation functions on test of Shwartz English dataset, and we include
HypeNET and Best Distributional results reported by Shwartz.

4.3.

Results for English

We include for comparison the results of the best distributional model reported by Shwartz et al. (Shwartz et al.,2016) and HypeNET integrated mdoel. In the Table 5 canbe seen how the order embedding achieves considerable
good results in comparison to the best distributional model
reported by Shwartz and also in comparison to HypeNET,
that is a pattern-based and distributional combined model.
We found interesting the good performance of the order embedding model taking as input general purpose word embeddings and without considering any explicit paths information on a corpus.
4.3.1. Results for Spanish
In this section we show the results obtained with the above
described model in the introduced dataset for Spanish. We
report order embedding results as a baseline in the dataset
for future comparisons.
In order to show the behavior of pattern-extracted and cohyponymy instances we consider the following different variants of the training data:

Figure 2: Order embedding diagram.

forward networks using pretrained fastText (Joulin et al.,2016) word vectors for Spanish and English.

• As base, the positive instances from WordNet and thetranslated instances of Shwartz dataset, and the negative instances randomly, sampling words from the vocabularies of Cardellino and WordNet. (OrdEmb)

We evaluate our models using precision, recall and F measures. The best configuration consisted on a three layered feed forward networks, with 150 neurons and SELU
activation function on the first two layers and 100 ReLU
units for the output layer. For the training we consider
Adam (Kingma and Ba, 2014), with a learning rate of
0.005, and we conclude the training by early stopping, witha patience of 5. We checkout the best performing model
against the validation set along the whole training.

• The base dataset adding cohyponyms as negative instances for training. (OrdEmb +cohyp)• The base dataset adding positive instances extracted
by patterns. (OrdEmb +pattern)79• The base dataset adding for training cohyponyms as
negative instances and pattern extracted pairs as positive. (OrdEmb +pattern+cohyp)2016 Nov 1-5; Austin, TX. Red Hook (NY): ACL; 2016.
p. 424-35. ACL (Association for Computational Linguistics).
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(2014). Learning semantic hierarchies via word embeddings. In ACL (1), pages 1199–1209. The Association
for Computer Linguistics.
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from large text corpora. In 14th International Conference on Computational Linguistics, COLING 1992,
Nantes, France, August 23-28, 1992, pages 539–545.
Joulin, A., Grave, E., Bojanowski, P., Douze, M., Jégou, H.,
and Mikolov, T. (2016). Fasttext.zip: Compressing textclassification models. arXiv preprint arXiv:1612.03651.
Kingma, D. P. and Ba, J. (2014). Adam: A method forstochastic optimization. CoRR, abs/1412.6980.
Levy, O., Remus, S., Biemann, C., and Dagan, I. (2015).
Do supervised distributional methods really learn lexical inference relations? In Rada Mihalcea, et al., editors, NAACL HLT 2015, The 2015 Conference of theNorth American Chapter of the Association for Computational Linguistics: Human Language Technologies,
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Nakashole, N., Weikum, G., and Suchanek, F. M. (2012).
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types. In EMNLP-CoNLL, pages 1135–1145. ACL.
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We show the obtained results in the table 4. We evaluate
the model against the base test partition and including cohyponymy instances on the test data. In the results can beobserved that both cohyponyms and pattern-extracted instances during the training give some improvement in mostcases, where cohyponyms are most beneficial, with the exception of the lexical split evaluating with cohyponyms addition in test partition.

5.

Conclusion

In this paper we show the results obtained on supervised
hypernymy detection in Spanish. Given the lack of resources in Spanish for hypernymy detection we build adataset based on previous work for English. We included
two versions of the dataset according to its train, validation and test partitions, and the lexical intersection between
them: random and lexical split. The former is done randomly while the lexical split does not contain lexical intersection between the partitions, tackling the lexical memorization problem of the hypernymy detection. We train anorder embedding using general purpose word vectors andwe obtain that considerable good results. We show the behavior of including cohyponyms pairs for the training considerably improves the overall result.

6.

Bibliographical References

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81

Proceedings of the 7th Workshop on Linked Data in Linguistics (LDL-2020), pages 82–86
Language Resources and Evaluation Conference (LREC 2020), Marseille, 11–16 May 2020c European Language Resources Association (ELRA), licensed under CC-BY-NC

Lexemes in Wikidata: 2020 status
Finn Årup Nielsen

DTU Compute, Technical University of Denmark
Richard Petersens Plads, Kongens Lyngby, Denmark
faan@dtu.dk
Abstract
Wikidata now records data about lexemes, senses and lexical forms and exposes them as Linguistic Linked Open Data. Since lexemes in
Wikidata was first established in 2018, this data has grown considerably in size. Links between lexemes in different languages can bemade, e.g., through a derivation property or senses. We present some descriptive statistics about the lexemes of Wikidata, focusing on
the multilingual aspects and show that there are still relatively few multilingual links.
Keywords: Wikidata, lexicographic data, Linguistic Linked Open Data

1.

Introduction

Wikidata is the structured data sister of Wikipedia
where users can collaboratively edit a knowledge graph(Vrandečić and Krötzsch, 2014). Wikidata does not only
support the different language versions of Wikipedia but
also the other Wikimedia wikis such Wikisource, Wikimedia Commons, Wikiquote, etc. as well as describe many
items without any equivalent article in the other wikis. For
instance, Wikidata describes tens of millions of scientificarticles (Nielsen et al., 2017). The data in Wikidata isconverted to a Semantic Web representation (Erxleben etal., 2014) and a public and continuously updated SPARQL
endpoint—Wikidata Query Service (WDQS)—is set up at
https://query.wikidata.org.
Since 2018, Wikidata has included special pages for
lexicographic data distinguished from the usual Wikidata“Q-items” with a new namespace for lexemes. Each page
represents one lexeme, its sense(s) and its lexical form(s)together with annotation about them and links between
them, both within and between lexemes as well as to the
Q-items. The lexicographic data is also converted to a
Semantic Web representation and available in WDQS.
For the RDFication of the lexeme data, Wikidata uses a
combination of classical Wikidata URIs and URIs from
(Linguistic) Linked Open Data ontologies (Cimiano et al.,2016; McCrae et al., 2017): ontolex:lexicalForm,
ontolex:sense,
ontolex:LexicalEntry,
ontolex:LexicalSense,
ontolex:Formand dct:language as well as other URIs, e.g.,
dct:langauage and wikibase:lemma.
We have described the lexicographic information on Wikidata before focusing on the Danish lexemes (Nielsen,
2019a) and also described our SPARQL-based Web application Ordia for aggregating and visualizing the Wikidata
lexicographic data (Nielsen, 2019b). Here we will make an
update of the work on lexemes in Wikidata and focus on themultilingual aspects.

2.

ChainsCount

Between-language count

1
2
3
4
5
6

3897
1158
443
141
47
12

1453
333
127
33
9
3

Table 1: Counts of level of etymological derivations(chains) per 23 February 2020. The last result is available
in WDQS from https://w.wiki/Htz.

able in Wikidata.2 This is up from 43,816 we reported in2019 (Nielsen, 2019a). In February 2020, there were over
3 million lexical forms and over 55,000 senses.

2.1.

Languages

Lexemes from 668 languages are recorded in Wikidata.3
However, many languages have only a single lexeme. The
top language with most lexemes is Russian (101,137 lexemes), followed by English (38,122), Hebrew (28,278),
Swedish (21,790), Basque (18,519), French (10,520) andDanish (4,565). Russian is also the language with more
forms than any other language (1,236,456), followed byBasque (956,473), Hebrew (446,795), Swedish (148,980),
Czech (77,747) and English (64,798). For senses, the languages from the top are Basque (20,272), English (12,911),
Hebrew (3,845), Russian (2,292) and Danish (2,217).

2.2.

Etymology

Etymological information may be described throughthe derived from property (P5191) corresponding tolemonet:derivedFrom from (Chiarcos et al., 2016).
It has been used over 3,800 times, see Table 1. Apart fromtracking derivations between different languages, the property may also be used to record intralanguage derivations.
Table 1 shows statistics for the total number of derivations
and the cross-language derivations by the derivation chain

Descriptions

In February 2020, Wikidata had more than 77 million Qitems.1 Over 250,000 lexemes are in February 2020 avail-2
https://tools.wmflabs.org/ordia/
statistics/
3
https://tools.wmflabs.org/ordia/
language/1
https://www.wikidata.org/wiki/Special:
Statistics

82

Figure 1: Derivation matrix: Count of the number of derived lexemes between languages as recorded in Wikidata.

length. The currently longest derivation chain is 6, and an
example of a long between-language derivation is from the
Afrikaans word hond (dog) through Dutch, Middle Dutch,
Old Dutch, Proto-Germanic to Proto-Indo-European.

ish (1,735), French (320), Polish (245) and English (197)
are the languages which have used the property the most.4
The etymological data in Wikidata is dwarfed by theamount that can be extracted from Wiktionary (de Melo,2014).

The etymological derivation matrix in Figure 1 shows theyet sparse between-language derivation data among the 25
languages with the most derivations. Most of the languages
are Indoeuropean, though among the 25 are also Samiclanguages, Malay and Interlingua. The largest number ofrecorded (direct) derivations is from Old Norse to Nynorsk,— but with just 189 links. Latin is the source languagewith the most derivations. A PageRank analysis in NetworkX of the directed and count-weighted derivation graphwith α = 0.9 presents Proto-Indo-European on the top, followed by Latin, Ancient Greek and English.

2.3.

Senses

Lexemes link to senses and a sense can link to senses inother languages. The two primary means are through thetranslated to property (P5972) that links to other senses
or by the item for this sense (P5137) that links to a Qitem. As of 26 February 2020, the former property has beenused 3,633 times, while the latter property has been used
25,891 times. Figure 2 shows the number of translations
4

Counting distinct lexemes with WDQS with the SPARQL?lexeme dct:language ?language ; wdt:P5238[] . with the result at https://w.wiki/J5x.

Derivations and compounding may also be described by thecompound property (P5238). As of 25 February 2020, Dan-

83

Figure 2: Sense-Q-item links between languages among lexemes in Wikidata. The diagonal shows twice the number ofsynonym combinations for lexemes within each languages. The data has been extracted with a SPARQL query that contains the following fragment: ?lexeme1 dct:language ?language1 ; ontolex:sense / wdt:P5137?item . ?lexeme2 dct:language ?language2 ; ontolex:sense / wdt:P5137 ?item.

via the item for this sense property for the 25 languages
with the most translation links. The diagonal shows twice
the number of synonym combinations for lexemes withineach language. The current number of translations is much
lower than what can be extracted from Wiktionary, see, e.g.,(Sérasset, 2014, Table 4). Only the combination EnglishHebrew has over 1,000 translations. While Basque is thelanguage with the most senses defined, the senses of thelanguage do not in a sufficient degree link further on to the
Q-items to get among the 25 most linked languages that isshown in Figure 2.

rather to the sense of the region associated with demonym,
e.g., from the French lexeme parisienne (L25620) to the
Q-item for Paris (Q90). Figure 3 shows the demonym matrix where the Spanish-Danish language pair has the largest
number of links between demonyms.

A different way to link senses to Q-items is by the demonymof (P6271) property that is only relevant to use for demonyms. It does not link to the sense of the demonym, but

Wikidata has numerous deep links to items in external
databases through the properties with the external identifier datatype. There are currently 4.789 recorded prop-

There are several other properties that link sense-to-sense
within language, e.g., hypernym, troponym of and a seldomly used periphrastic definition property.

2.4.

84

External identifiersApart from these identifiers, Wikidata has identifiers to linkits Q-items to BabelNet (P2581) and for the Interlingual Index Identifier (P5063) (Navigli and Ponzetto, 2010; Bond etal., 2016). They receive 61,378 and 31 links, respectively.

2.5.

Figure 3: Sense-Q-item links between languages amonglexemes in Wikidata with the demonym for property. The
diagonal counts the number of distinct lexemes per language with demonym senses.
Count

Identifier

Language(s)

14440
2878
1688
1353
1288
1027
1009
388

Elhuyar
DanNet word
WSO Online
SJP Online
Doroszewski
Dobry słownik
WSJP
Oqaasileriffik

BasqueDanishPolishPolishPolishPolishPolishGreenlandic, Danish,
EnglishItalianEnglishPolish

216
212
160

Vocabolario Treccani
OED Online
Kopaliński

Other linguistic data in Wikidata

Wikidata can describe linguistic resources and use them toannotate lexemes. Datasets, corpora and dictionaries may
have entries in Wikidata. Ordia shows resources that havebeen used in the usage examples for the lexemes of Wikidata.6 The National Corpus of Polish (Q6971865) and theEuroparl (Q5412081) corpus (Koehn, 2005) are the two resources that have been used the most.
Wikidata’s Q-items may link to lexeme items with the subject lexeme (P6254). 824 distinct Q-items makes 831 links
in total. Most of these Q-items describe Wiktionary pages
for French conjugations. A few other items describe scientific papers that focus on particular lexemes, e.g., the new
Swedish pronoun hen discussed in (Tavits and Pérez, 2019).

3. Discussion
The amount of lexeme data in Wikidata continues to grow,but in many aspects the extent is still low and the annotation for etymology and senses is meager. Russian lexemes and forms are exceptions. They have been automatically set up from the Russian Wiktionary with the Lexicator
tool.7 Wikidata requires the permissive Creative Commons
Zero license for its data and this may have prohibited theset up of lexicographic data from other resources, including sharealike-licensed Wiktionary.
What might also have held the sense data growth back is theunresolved issue of linking non-noun lexemes. Q-items in
Wikidata usually correspond to common or proper nouns,— at least their labels are usually nouns. The question is
how lexemes corresponding to verbs, adjectives and adverbs should be linked. Take the example of the English
adjective little: Should a Q-item for smallness be created
and the little lexeme linked to that item by the P5137 property, should there be a separate Q-item for little linked by
P5137, or should the little lexeme be linked to a smallness
by some other means? Wordnets may link lexicographic
items across part-of-speech classes with, e.g., derivationally related form or pertainym.
The tool Wikidata Lexeme Forms,8 that works for several
languages, helps Wikidata lexeme editors create lexemes
and their forms. We have set up several ShEx expression
to detect errors of omission and commission or diversions
from normal use for Danish lexemes (Nielsen et al., 2019).
Such tools help Wikidata editors maintain a form of consistency and comprehensiveness within each language.

Table 2: External identifiers in Wikidata sorted according to usage per 22 February 2020. Updated statistics is available at https://tools.wmflabs.org/
ordia/statistics/

erties of this type.5 A few of these relate to the lexicographic items, potentially making Wikidata a multilingual hub for lexicographic resources. The statistics
page in Ordia at https://tools.wmflabs.org/
ordia/statistics/ shows statistics on 19 linguistics
external identifiers. Only 11 of these have currently morethan 100 links and they are shown in Table 2. The Elhuyar identifier for a Basque online dictionary has by far themost identifiers. The second most frequent identifier is forwords within the Danish wordnet DanNet (Pedersen et al.,2009), and then follows several identifiers for the Polishlanguage. The Greenlandic Oqaasileriffik online dictionary
records both Greenlandic, Danish and English lexemes.

6
https://tools.wmflabs.org/ordia/
reference7
https://github.com/nyurik/lexicator.
Issues about what is copyrightable lexicographical datain the context of Wikidata has been discussed, see, e.g.,https://meta.wikimedia.org/wiki/Wikilegal/
Lexicographical_Data.
8
https://tools.wmflabs.org/lexeme-forms/

5
https://www.wikidata.org/wiki/Category:
Properties_with_external-id-datatype

85

4.

Conclusion

Nielsen, F. Å. (2019a). Danish in Wikidata lexemes. Proceedings of the Tenth Global Wordnet Conference, pages
33–38.
Nielsen, F. Å. (2019b). Ordia: A Web application for
Wikidata lexemes. The Semantic Web: ESWC 2019
Satellite Events, pages 141–146, May.
Pedersen, B. S., Nimb, S., Asmussen, J., Sørensen, N. H.,
Trap-Jensen, L., and Lorentzen, H. (2009). DanNet: thechallenge of compiling a wordnet for Danish by reusing amonolingual dictionary. Language Resources and Evaluation, 43:269–299, August.
Sérasset, G. (2014). DBnary: Wiktionary as a LemonBased Multilingual Lexical Resource in RDF. Semantic
Web: interoperability, usability, applicability.
Tavits, M. and Pérez, E. O. (2019). Language influences
mass opinion toward gender and LGBT equality. Proceedings of the National Academy of Sciences of theUnited States of America, 116:16781–16786, August.
Vrandečić, D. and Krötzsch, M. (2014). Wikidata: a freecollaborative knowledgebase. Communications of theACM, 57:78–85, October.

The lexicographic data in the lexeme part of Wikidata is yet
not extensive in most aspects, but continuously growing.
The most represented languages are Indoeuropean, particularly Slavic, Germanic and Romance languages. Links
between lexemes of different languages can be establishedby an etymological property as well as through senses and
the Q-items of Wikidata and links to external lexicographic
resources can be established by several external identifier
properties in Wikidata.

5.

Acknowledgements

This work is funded by the Innovation Fund Denmark
through the projects DAnish Center for Big Data Analytics driven Innovation (DABAI) and Teaching platform fordeveloping and automatically tracking early stage literacy
skills (ATEL).

6.

Bibliographical References

Bond, F., Vossen, P., McCrae, J. P., and Fellbaum, C.
(2016). CILI: the Collaborative Interlingual Index. Proceedings of the Eighth Global WordNet Conference,
pages 50–57, January.
Chiarcos, C., Abromeit, F., Fäth, C., and Ionov, M. (2016).
Etymology Meets Linked Data. A Case Study In Turkic.
Digital Humanities 2016: Conference Abstracts, pages
458–460.
Cimiano, P., McCrae, J. P., and Buitelaar, P. (2016). Lexicon Model for Ontologies: Community Report, 10 May
2016. May.
de Melo, G. (2014). Etymological Wordnet: Tracing The
History of Words. Proceedings of the Ninth International Conference on Language Resources and Evaluation, pages 1148–1154, May.
Erxleben, F., Günther, M., Mendez, J., Krötzsch, M.,
and Vrandečić, D. (2014). Introducing Wikidata to the
Linked Data Web. The Semantic Web – ISWC 2014,
pages 50–65.
Koehn, P. (2005). Europarl: A Parallel Corpus for Statistical Machine Translation. The Tenth Machine Translation
Summit: Proceedings of Conference, pages 79–86.
McCrae, J. P., Bosque-Gil, J., del Rı́o, J. G., Buitelaar, P.,
and Cimiano, P. (2017). The OntoLex-Lemon Model:
Development and Application. Electronic lexicography
in the 21st century. Proceedings of eLex 2017 conference., pages 587–597.
Navigli, R. and Ponzetto, S. P. (2010). BabelNet: building
a very large multilingual semantic network. Proceedingsof the 48th Annual Meeting of the Association for Computational Linguistics, pages 216–225, July.
Nielsen, F. Å., Mietchen, D., and Willighagen, E. (2017).
Scholia, Scientometrics and Wikidata. The Semantic
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86

Author Index
Abgaz, Yalemisew, 61
Abromeit, Frank, 36
Ahmadi, Sina, 45
Bellandi, Andrea, 70
Borin, Lars, 23
Cimiano, Philipp, 28
Costa, Rute, 45
di Buono, Maria Pia, 28
Elahi, Mohammad Fazleh, 28
Etcheverry, Mathias, 75
Fäth, Christian, 36
Fernandez Sanchez, Daniel, 75
Fiorelli, Manuel, 52
Forsberg, Markus, 23
Gillis-Webber, Frances, 1
Giovannetti, Emiliano, 70
Glaser, Luis, 36
Grimm, Frank, 28
Hammarström, Harald, 23
Khan, Fahad, 15
Lee, Gun Woo, 75
McCrae, John Philip, 45
Mondaca, Francisco, 11
Nannini, Alessandro A., 1
Nielsen, Finn, 82
Rau, Felix, 11
Salgado, Ana, 45Simões, Alberto, 45
Stellato, Armando, 52
Tittel, Sabine, 1
Virk, Shafqat Mumtaz, 23
Wichmann, Søren, 23
Wonsever, Dina, 75
87